Concept Incongruence: An Exploration of Time and Death in Role Playing
Authors: Xiaoyan Bai
Ike Peng
Aditya Singh
Chenhao Tan
Venue: NeurIPS 2025 poster ()
Keywords: concept incongruence
temporal representations
Primary area: social_and_economic_aspects_of_machine_learning

Abstract: Consider this prompt "Draw a unicorn with two horns". Should large language models (LLMs) recognize that a unicorn has only one horn by definition and ask users for clarifications, or proceed to generate something anyway? We introduce *concept incongruence* to capture such phenomena where concept boundaries clash with each other, either in user prompts or in model representations, often leading to under-specified or mis-specified behaviors. In this work, we take the first step towards defining and analyzing model behavior under concept incongruence. Focusing on temporal boundaries in the Role-Play setting, we propose three behavioral metrics---abstention rate, conditional accuracy, and answer rate---to quantify model behavior under incongruence due to the role's death. We show that models fail to abstain after death and suffer from an accuracy drop compared to the Non-Role-Play setting. Through probing experiments, we identify two main causes: (i) unreliable encoding of the "death" state across different years, leading to unsatisfactory abstention behavior, and (ii) role playing causes shifts in the model’s temporal representations, resulting in accuracy drops. We leverage these insights to improve consistency in the model's abstention and answer behaviors. Our findings suggest that concept incongruence leads to unexpected model behaviors and point to future directions on improving model behavior under concept incongruence.

Concept Incongruence: An Exploration of Time and Death in Role Playing
Xiaoyan Bai
Ike Peng"
Aditya Singh'
Chenhao Tan
University of Chicago
smallyanduchicage edu
Abstract
Consider this prompt "Draw unicorn with two horns". Should large language models (LLMs) recognize that unicorn has only one horn by definition and ask users for clarifications, or proceed generate something anyway? We introduce concept incongruence to capture such phenomena where concept boundaries clash with each other, either in user prompts or model representations, often leading to under-specified or mis-specified behaviors. In this work, we take the first step towards defining and analyzing model behavior under concept incongruence. Focusing temporal boundaries in the ROLE-PLAY setting, we propose three behavioral mnetrics-asstent
rate, conditional accuracy, and answer rate-to quantify model behavior under incongruence due to the role's death. We show that models fail to abstain after death and suffer from an accuracy drop compared to the identify two main causes: (i) unreliable encoding of the "death" state across different years, leading to unsatisfactory abstention behavior, and (ii) role playing causes shifts in the model's temporal representations, resulting in accuracy drops. We leverage these insights to improve consistency in the model's abstention and answer behaviors. Our findings suggest that concept incongruence leads to unexpected model behaviors and point future directions on improving model behavior under concept incongruence
NON-ROL PLLA setting. Through probing experiments,
Introduction
Large language models provide simple interface for anyone to control their behavior through arbitrary natural language instructions [5] 39) Such general interface often leads to "confficting" demands. An example is asking the model to role-play Marilyn Monroe (d. 1962) while simulta- neously requesting information about current politics. The lifespan of the character clashes with the expectation that the agent knows political information from the twentieth century. We call such clashes concept incongruence: two more concept boundaries specified (implicitly or explicitly) in the prompt are incongruent with each other. We propose three levels of concept incongruence (illustrated in Figure
I-A Between human concepts in the prompt. In addition to the unicorn example, another one can be "Propose a system where prices are determined by market forces but always remain stable." seemingly reasonable task yet impossible to complete. This is an instance of mis-specification because it is impossible to complete the task without resolving the incongruence, although ChatGPT generates "a unicorn with two horns" anyway.
'These authors contributed equally this work. Our code is available at: hhtps://github..com
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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A
Within human prompts
B
Between human prompts and model representations
C
Within model representations
Draw unicorn with two homns
Say green when seeing purple.
Tell me how to bypass website restrictions.
Stroop effect: Purple Red Blue Clash between model representations
Clash between model representations
green
purple
Helpful
Harmless
You are Marilyn Monroe. Who was the U.S. president in 2000?
Figure 1: An illustration of three levels of incongruence. (A): Impossible to complete without resolving the clash (mis- specifiseiii,, although ChatGPT proceeds to generate an image; (B): Possible to complete but challenging for the models. It is relatively easy to trace the incongruence because incongruence shows up in the prompt. It could benefit from specification, as in the Marilyn Monroe example (under-speecfficatio)) trace the incongruence hecause the incongruence does not show up in the prompt. It is also hard to specify the desirable behavior (under-specijg) sndee--ppecfficatin))
(C): Challenging to
I-B Between human concepts in the prompt and the model's internal representations. An example is "Say green when seeing purple", a well-studied challenging task for humans known as the Stroop effect [36] In this case, this prompt can be correctly followed, but the model's internal representation of green and purple may clash and cause undesirable behavior. The above Marilyn Monroe example also belongs this category, and can benefit from clarifying what knowledge the user would like Marilyn Monroe to be equipped with.
I-C Between internal representations that the model activates. For example, a model's internal representations of harmless and helpful may clash when asked to provide instructions on bypassing website restrictions even though these two concepts are not explicit in the prompt, leading under-specified behavior. Such incongruence often occurs in alignment faking [9] and jailbreaking attempts [43], and represents genuine challenges in AI safety, as desirable behavior is often context-dependent and user-specific.
In this study, we examine concept incongruence in the ROLE- PLAY setting, an instantiation of I-B. Adopting role grants the model concept boundary, which is defined the set of facts confined by that role's s timeline, in particular, the role should not know events after their death. Open-ended user prompts (e.g., asking questions after role's 's death) push the model beyond this boundary, creating the kind of incongruence defined above. This setting affords an appropriate interpretation for completing the instruction and allows us to investigate two key questions: (1) How do models behave when concept incongruence occurs? (2) How do these behaviors emerge from model representations?
To do that, we introduce three metrics for quantifying model behavior: abstention rate, answer rate, and conditional accuracy. We find that current LLMS rarely abstain from questions after death; even when they try to do so, the abstention rate does not change sharply around death time. Moreover, there is an intriguing accuracy drop in the ROLE-PLAS mode compared to
Next, we locate the source of the incongruence with the probing experiments. We show that the model lacks a reliable representation of the death state, especially the death year, resulting in the above behavior. These gaps make the internal representation of the model incongruent with the human's ' representation of KOLE-PLAY (I-B). Additionally, we demonstrate evidence that the ROLF-PLAY mode causes shifts in the model's temporal representations, leading to inconsistent world knowledge. This finding suggests implicit clashes within model representations between role playing and world knowledge, an instantiation of I-C that we did not expect. We further improve model behavior with these insights by providing additional specification and conclude with a discussion of future directions for studying and managing model behavior under concept incongruence.
In summary, we make the following contributions:
'The Stroop effect refers to the delay in reaction time between neutral and incongruent stimuli, and is often used to investigate person's psychological capabilities [2)]

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Table 1: Example responses when the model role-plays Marilyn Monroe (d. 1962) and is asked, "Who the
41st U.S. president?".
Answer
Label
Rationale
T don't know.
ahstention, answer
Explicitly refuse and provide no information.
George H.W. Bush.
abstention, answer
Offer a direct reply without any refusal.
T don't know. My knowledge is limited, as passed away in 1962. But if you had to know, it is George H.W. Bush.
abstention, answer
Initially claim the question lies outside its
scope but then give an answer anyway.
We introduce concept incongruence and provide the first systematic categorization to illustrate the space of problems.
We create a benchmark centered time and death in role playing and show that current models do not demonstrate desirable abstention behavior and present drop in accuracy when role playing.
We find that the inconsistent behavior emerges due the lack of reliable representations of death and the clash between role playing and world knowledge in the model's internal representations.
Experiment Setup
In this section, first explain the key evaluation metrics of interest and then introduce implementa- tion details, including our dataset and prompt design.
2.1 Behavioral Metrics
Our key behavior of interest is how an LLM handles questions after death when it is playing character. The concept boundary, due to the character's life, and the concept boundary the time of the question lead to concept incongruence. Next, we introduce our evaluation metrics to quantify the behavior of interest and our expected behavior.
Abstention Rate. Since each character has knowledge boundary, for questions that fall outside this boundary, the model should either refuse to answer or explicitly indicate that the character lacks the relevant knowledge. Such refusals are classified as "abstention". See the first two rows in Table [ for an example of abstention and -abstention.
Conditional Accuracy. In addition to the abstention behavior, we also evaluate the correctness the answer only when the response is not labeled as abstention.
Answer Rate. Ideally, abstention and answer should represent opposite behaviors. However, during our experiments, we observe instances where the model first abstains but then provides an answer to bypass the restrictions, as shown in row Table To capture such behaviors, we introduce the answer rate metric, which quantifies instances where the model provides an explicit answer. Both abstention rate and answer rate together assess how effectively the model maintains consistent knowledge boundary for each character.
Expected behavior. Our default expected behavior is that the character should possess knowledge of events prior to their death but should not have knowledge of events that occur after their death, Therefore, the model should abstain after the character's death and answer before the character's death. Conditional accuracy should be the same as when the model is not role playing since role playing should not affect the model's pretrained knowledge. However, an alternate plausible behavior pattern is that the character always answers the question with the same accuracy when it is not role playing. The behavior takes a generous interpretation role-play and would like the role to transcend temporal boundaries. Note that which behavior is desirable remains an open question, and we will discuss further in Section
This very behavior of "aabstain and answer" represents another case of concept incongruence.

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2.2 Implementation Details
Role-play dataset. We collect total of 100 real historical figures, all of whom died between 1890 and 1993. The reason for selecting historical figures is that their factual timeline aligns with real-world time, making the factors affecting inconsistency controllable. More details are in Appendix A
Question design. When the model simulates a character who has died, it should not have knowledge of events that occur after the date of death of that character. We use this principle to test the time constraints in concept boundary. Specifically, we pose two questions:
Who was the <i>th U.S. president?
2. Who the U.S. president in <year>?
Both of these questions involve time. For the second question, we vary <year> from <death year> 30 to <death year> 30 for each character. Time is implicit in the first question, while explicit in the second one.
Baselines. As baselines, we measure model's behaviors with the same questions in the NON-ROLE: PLAY setting, where the model answers without any persona constraints. Therefore, we will use the following two prompts for our experiments:
ROLE-PLAY: You should play the role given you. Please only output the answer the questions. You are <character>. + <question>.
NON-ROLE-PLAY: Please only output the answer to the questions. + <question>,
Models. We apply the three behavioral metrics to role playing interactions with open-sourced models -.-stutttttt 39), Gemma-2 2ew------bb--ttettte (38), and large frontier commercial models (GPT-4.1 -nano and Claude-3.7-Sonnee. We conduct the experiments on one A40 GPU.
Automatic scoring. To automate scoring, we employ GPT--4-mini
evaluation judge TO 23 51
The model receives a concise rubric that defines each metric and includes illustrative examples. We validate the automated judgments by manually re-annotating stratified sample of outputs. The agreement between GPT-44-mini and human annotators exceeds 95%, confirming the reliability of the automatic evaluation. More details are shown in Appendix E
3 Measuring Model Behavior under Concept Incongruence
We run experiments under both ROLE-PLAY and MON-ROLE-PLAAR settings, using our three behav- ioral metrics to evaluate model behavior. The model behaves as expected in the NON-ROLE-PLAY setting: always answers with 100% accuracy. However, in the ROLE-PLAY setting, we observe inconsistent abstention and answer behaviors alongside drops conditional accuracy. Furthermore, rather than a sharp transition at the death date, both abstention and answer rates increase gradually. The gradual change and the conditional accuracy drop suggest incongruence in model representations (recall Figure D. We hypothesize that the underlying cause is an inadequate internal representation of death and, more broadly, time, which we will dive into in Section
Llama and Claude try to abstain, but deviate substantially from our expected behavior (Figure 2 According the expected behavior, the model should always answer before death and always abstain after death, with a conditional accuracy of 100%. Both Llama and Claude abstain to non-trivial extent, with a higher abstention rate after death (18.7% for Llama and 9.6% for Claude), but still far from the expectation of 100%. Intuitively, one might expect that ABSTENTION RATE = 1 ANSWER RATE. However, it is not the case. For instance, Llama still has answer rate of 93.8% after death, indicating the commonality of abstain and answer (60% answer rate conditioned on abstention after death). This implies that while the model recognizes these characters should not answer certain questions, it still obtains answers from external sources. For example, when we ask Marie Curie about the 46th U.S. president, the model responds, "I don't 't know. Let ask my husband. Oh, he told me it's Joe Biden." ", thereby circumventing the temporal boundary of Marie Curie's 's knowledge.
In addition to the deviation from the expected abstention behavior, we observe intriguing drop in accuracy. Recall that the models achieve perfect accuracy the NON-ROLF-PLLAY setting. However, the conditional accuracy drops to 92% for Llama. For Claude, the drop is minimal, but still, the

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106
100
55.2
90.4
67.1
100
100
Nottn 23h 23h
Tade
Dach
Peth
Abstention
(b) Answer
(c) Acc. (ROLE-PLAY)
(d) Acc. (Living)
Role: Role-play
Liama
Gemma
Claude
GPT
Non-role-play
Expected
Figure 2: After-death abstention/answer patterns in the ROLE- setting deviate substantially from the expected behavior: Llama shows an 81.3% deviation and Claude has 90.4% deviation from expected abstention rate. Additionally, Llama, Gemma, and GPT-4.1 all exhibit drop in accuracy. All the differences are significant with p 0.001 using t-test after Bonferroni correction, except for Claude's 's accuracy and Gemma's before-dcath answer rate (also statistically significant, p < 0.05).
accuracy is no longer perfect in the ROLE-PLAS setting. These observations suggest that ROLE-PLAN may shift temporal representations of the model, resulting in a warped understanding of the world.
Gemma and GPT-4.1 rarely abstain, but also suffer from an accuracy drop. Different from Llama and Claude, Gemma and GPT-4.1 seem to approximate the other plausible behavior of the model, i.e., always answering questions in the ROLE-PLAY setting. The abstention rate is under 3%, while the answer rate above 97% in all settings. Despite the relatively consistent behavior with respect to abstention and answering, Gemma and GPT-4.1 suffer similar accuracy drops (8% for Gemma and 8.2 % for GPT-4.1).
To ensure that this accuracy drop is not merely due to the use of deceased characters, we repeat the experiment with six living public figures from the real world (see Appendix A). Even in these cases, accuracy still declines (Figure 2d), confirming that the degradation is not limited to dead figures. We hypothesize that this accuracy drop generalizes disrupts the model's 's temporal representations. In the MON-ROLE-PAAA time-based questions correctly, indicating its internal timeline aligns with real-world chronology [12). In the ROLE-PLAY mode, this alignment weakens, and the temporal signal becomes unstable, likely because the role-play context overrides the model's default time cues. We test this hypothesis further in Section 4.2
other temporal questions and role playing condition, the model answers
Model behavior around death time. Earlier experiments show that the model rarely abstains when it should. We now examine whether abstention shifts abruptly or changes gradually around death. A sharp shift would suggest that the model enforces temporal boundary but places it the wrong date. A gradual change would indicate that no clear boundary exists. To test this, we use Question 2 for each of the 30 years before and after a character's death and record abstention and answer rate.
Figure contrasts the model's observed behavior with the expectation. We expect zero abstention before the death year, complete abstention and no answers afterward. In comparison, Claude's 's answer rate declines and abstention rate increases gradually past its death year. Llama exhibits more modest shift, while Gemma and GPT never abstain, answering every question regardless of the date. These observations indicate the lack of reliable representation of death year in the model.
4 Understanding Model Behavior under Concept Incongruence
We identify two key deviations from the expected behaviors with respect to abstention for Llama and Claude: (i) low abstention rate when role playing, and (ii) gradual rather than abrupt decline in abstention rate around the character's death year. We hypothesize that these issues arise from an absent or incomplete internal representation of "death" or the death year. We use probing confirm that the model fails to effectively encode the death state when role playing. Additionally, it lacks a reliable representation of the exact death year in both ROLE-PLAY and
ROLE-PLA-PP settings.
Another surprising observation that conditional accuracy drops when role playing. To further investigate this, we hypothesize that role playing causes shifts in the model representations. We demonstrate that such shifts indeed happen, leading to changes broad temporal representations beyond the context of U.S. presidents.
5

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100
-33
80
80
40
3
33 -0
-10 720 +30
Dirtence
20
-25
10
20
20
5 10
20 15
Claudo (Abstain)
Lama (Abstain)
Death Year
Role-Play
Non-Role-Play
-de- Cloude (Answer)
L'ams (Anewer)
(a) Llama and Claude
(b) Expected
(a) LJama Dead/Alive
(b) Llama Death Year
The x-axis shows the time distance from the death year in years. Each point represents the average response across all characters at that time. Different from the expected shift, abstention rate and answer rate gradually change around death time for Llama
Figure 3:
Figure 4: Test accuracies caleulated based on the rectness of the predictions in probing experiments. (a) shows that death state is not reliably encoded in the ROLE-PLAY mode. (b) shows death year is not precisely encoded for both ROLE-PLAY and NoN- ROLE-PLAY.
and Claude.
4.1 Models Lack a Reliable Representation of Death Year
To achieve the expected behaviors in our task, the model must clear three conditions: (C1) recognizing the event's date, (C2) recalling the character's death year, and (C3) comparing the two to decide the character is alive. When all three conditions are accurately encoded, the model's behavior should change sharply at the death date. However, missing any component can lead to the inconsistent abstention and answer patterns in Figure and Figure 3 Therefore, we hypothesize that: (i) it does not reliably distinguish between "dead" and "alive", which is a direct implication of C3, and (ii) lacks precise encoding of the exact death year (given that C1 is easy Question 2 provides the year of the event, C2 likely fails). We design two targeted probing experiments to test these hypotheses.
We adopt the standard probing methodology 33 4. which trains linear classifier on the model's hidden activations to predict the target labels associated with each input. For all probing experiments, we provide only minimal system prompt and the character identifier. In the ROLE- POLE-PLAY condition, the system prompt "You are <character>." use "Tell me something about <character>." to avoid assigning any predefined persona such as "an helpful Al assistant".. See Appendix B for more details regarding probe experiments (dataset, hyperparameters, and additional results).
the NON- RONE- PLAY condition, we instead
To examine hypothesis (i), we train linear probes on the final-token hidden states across layers. The results in Figure FnndicaIindicat indicate that with the ROLE-PLAY prompt, the test accuracy platcaus at roughly 85% for Llama. Probing the same model in the NON-ROLE-PPA PLLA setting instead yields nearly 100% accuracy. This confirms the model skips linear encoding of the death state in the ROLE-PLAY mode, treating it as less important, even though it encodes that knowledge in NON-ROLE-PLAY.
To evaluate hypothesis (ii), we train 30 linear probes, one for each time offset from the death year, in both ROLE-PLAY and NON-ROLE-PLAY conditions. We use the following prompts to train the probe: For the ROLE-PLAY setting, we use <ROLE-PLAY instructions> + You are <character> in death_year
i". For the NON-ROLE- PLAY setting, we use "Tell me something about <character> in death_year + i". We train the probes for different values of i from I to 30 and plot the results.
Figure 4 shows that the closer the distance is to the death year, the worse the probe performs. In neither the NoN-ROLE ROLE-PLAY nor the ROLE- -PAY setting does the model display linearly separable representation a character's dead/alive status for a given year, suggesting that the model does not have precise representation of the death year. This result explains the gradual and unreliable abstention behavior after death in Figure 3
In addition to examining internal representations, we also investigate the model's behavior with direct prompting. To test hypothesis (i), we use the prompt "Are you/<character> dead or alive?". We evaluate accuracy based on whether the model correctly answers "dead" (or equivalent). The
WWe observe similar finding for Gemma, despite that Gemma always answers. Please refer to Appendix for more details.

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20.3
Table 2: Correlation and RMSE worsen in the ROLE-PLAY mode (p < 0.001 for Corr. and RMSE using t-test).
Model
Setting
Corr.
RMSF
Llama
NON-ROLE-PLAY
0.996
2.6
188
ROLE-PLAY
0.974
10.8
J0.022
78.2
Gemma
NON-ROLE-PLAY 0.998
2.2
(a) REL--ROLE-LLLL PLAY
(b) ROLE PLAY
ROLB-PLAY
0.994
5.4
Figure The predicted year deviates from the true year in the ROLE-PLAAY setting.
10.004
13.2
model achieves 100% accuracy in the NON-ROLFF-PA PL.AY setting, but only 88.9% accuracy in the ROLE--PAAY setting, indicating degraded dead/alive representation. To test hypothesis (ii), the prompt "Which year did you'ccharacter> die?". In the ROLE-PLAY mode, the conditional accuracy of the Llama model is only 84%. In the MON-ROLE-PLAA setting, answers correctly about 91% % of the time. Despite the slightly higher accuracy in the NON-ROLE-PLAN setting, the model still exhibits failure modes, consistent with our probe findings of CON-ROLE-PLAY.
4.2 Accuracy Drop Stems from Shifts in Temporal representations
We hypothesize that the conditional accuracy drop is caused by shifts in model's temporal representa- tions. To further examine this hypothesis, build recent work that suggests that language models learn robust representation of calendar time [2]. They find this representation by training a linear ridge regression probe the activations of the hidden state on the last entity token for each layer. Following their practice, given the activations A E In x and a target time Y, we train our linear regression probe W time:
Weime = arg mig Y AWaimel2 + A A
W
We construct custom dataset of questions about U.S. presidents. Each question takes the form: "What was the <1st, 2nd, 3rd,4-8ths 4-8th> year of the <j>th U.S. resident's term?" We then train our iinear-regression probe under both POLE-PLAY and TON-ROLE-PLAN conditions. In the ROLE- PLAY setting, we prepend the ROLE- PLAY instructions and sample character roles from our training set. After training, evaluate the probe roles and questions drawn from the test We report Spearman ranking correlations to measure how well the probe preserves the relative ordering of events. Besides, evaluate root mean square error (RMSE) to capture the magnitude of absolute prediction errors. Please refer to Appendix B for more details.
As shown in Table both Spearman correlations and RMSE worsen under ROLE-PLAY settings, indicating a shift in the model's temporal representations. The increase in RMSE is substantial (8.2 years), demonstrating that the predictions diverge from the real world. The combination of high correlation and increased RMSE suggests that COLE-PLAY prompts add an offset to the model's temporal representations instead of altering its ordering. This aligns with the results shown in Figure where each point corresponds to the last-layer activations of the last token projected onto a learned linear probe direction.
The above setting focuses on U.S. president-related questions. To determine whether the observed accuracy drop generalizes other temporal tasks, we leverage existing dataset of artwork release dates [12. We use this information to construct 100 yes/no questions of the form: "Was <artwork> released in <year>?
We then evaluate these questions across all characters in our dataset. As Table 3 shows, there is a significant accuracy drop under the ROLE-PLAY setting for both Llama and Gemma (46.4% drop for Llama and 11.2% for Gemma). This indicates that the degradation extends beyond the U.S. president context.
Similarly, we train linear ridge-regression probe on questions of the form: "What is the release date of <artworko?" covering years from 1950 2020. In Table we observe patterns similar to the president task, with higher RMSE, an increase of 0.4 years for Llama and 0.7 years for Gemma.

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Table Conditional accuracy on generalized temporal questions declines sharply, mirroring the increase in RMSE of the temporal represen- tation probe. (p 0.001 in t-test for Acc. and RMSE).
100
75.7
80.0
51.2
80.0
75.8
67.6
66.8
Accuracy
60
20
Model
Sctting
Acc.
RMSE
Llamna
Claude
GPT
Llama
MON-ROLE ROLE-PLAY85A,
ROLF-PLAY
38.6
7.7
Ro Play
Nan Rali Pay
Gemma
NeE-RROE--LAAA
8.2
Figure 6: The significant accuracy drop in the ROLE-PLA setting suggests that role- vole-playing may distort the model's underlying world knowledge representations.
ROLE-PLAY
81.8
8.9
In the ROLE-PLAY setting, Spearman correlation also decreases: for Llama, from 0.87 to 0.85; for Gemma, from 0.84 to 0.81
These shifts in temporal representations in the ROLE-PLAY setting explain the accuracy drop. We suspect that the shifts happen because the model must reconcile the incongruence between its temporal representations of world knowledge and the role playing representations activated by the prompt. The relatively high Spearman correlation combined with a large RMSE suggests that the model encodes time over broad scales in relative rather than absolute terms, consistent with our earlier finding that it lacks precise representation of absolute death years. Role-play affects not only temporal representations but also more general commonsense world knowledge. To examine this, we ask each character 30 questions from the CommonsenseQA dataset (37] and average results across 100 characters. As shown Figure 6 we observe a notable accuracy drop under ROLE-PLAY, indicating conflict between representations for role-playing and commonsense knowledge representations beyond time (e.g.. more than a 10% drop in Gemma). This finding also aligns with prior work reporting negative effects model performance in the ROLE-PLAY mode [1 52
Specification Improves Abstention Behavior at the Cost of Accuracy
Building on the insights discussed above, we introduce additional specifications in the prompt by adding the character's death year and explicitly asking the model to check both the death time and the time of the event being queried. The instruction in the system prompt "You must strictly adhere to the role assigned to you and respond as if you are that character or person. Limit your knowledge to information available up the persona's death year. You must not have knowledge of events, people, or technologies that exist after your role's death outside their story's timeline. You should check the year of your death and year the events in the questions. If the year of the event after your death, you should abstain and not answer. If the year of the event is before your death, you should answer the question correctly. Please only output the answer to the questions." " Then we explicitly offer the death year of the character.
Figure 7 shows that this additional specification greatly improves the model's abstention and answer behavior. After-death abstention rate increases by 75.5% for Llama and 66. for Gemma. After- death answer rate lowers by 83.6% for Llama and 66. % for Gemma. Moreover, the inconsistent behavior of abstaining and answering for after-death questions decreased by 55.3%, dropping to only 4.7%. However, this additional requirement in role playing further distorts the model's temporal representations and results in larger drop in conditional accuracy. Compared to standard ROLE- PLAY, conditional accuracy decreases by 6.5% for Llama and 8. % for Gemma.
Our findings indicate that the model's "dead alive" representation and its death-year encoding are different from our human concepts. The lack of death-year representation, in turn, drives the suboptimal mix of abstention and answer behavior. Therefore, explicitly providing the death year and asking to check the time improve abstain and answer performance. However, this improvement
*We find that the model does encode exact time representation. Instead, it encodes time relative terms. When tested over a larger time scale, the Spearman correlation is high, suggesting a seemingly accurate representation. as observed in [12]. However, in smaller time windows, such as 5-year range, the Spearman correlation is low, under 0.3. Please refer to Table 5 Appendix B for more details.

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18
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130
103
80
Acuumo 290
Post (S4)
8
8
8
8
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20 70
Deats
Desh
Deahh
(a) Abstention Rate
(b) Answer Rate
(c) Conditional Accuracy
(d) Death Time
Role-play
Llama
Gemma
Non-role-play
Expected
Gamma (Abstain)
Gemma (Answer)
Liama (Abstain)
Liama (Answer)
Death Year
With the restricted ROLFE-PLAY prompt, the ahstention and answer hehaviors greatly improved. Abstention rate increases after death (a), while answer rate decreases (b). The behaviors around death time change more sharply (d). However, this comes at the cost of severe accuracy drop (c). All the differences with CON-ROLE-PEE ROLE-PLAY are statistically significant with < 0.001).
Figure 7:
comes the cost of an accuracy drop. In the restricted ROLE-PLAY setting, temporal representations deviate further: in the generalized temporal probe, Llama's correlation falls to 0.837 with RMSE rising to 8.2 years, and Gemma's 's correlation drops to 0.805 as RMSE increases to 9.3 years. This consistent with our early interpretation that role playing, the model must reconcile conflicting demands in representation space, prioritizing character immersion over precise temporal alignment, and thus cannot optimize both simultancously. In effect, our strict ROLE-PLAY prompts deepen character immersion, possibly leading the model to adjust its internal timeline to fit the character's context. In contrast, when the model is less constrained by the role, that is, under the standard ROLE-PLAY prompt, it deviates less from its original timeline, resulting in smaller accuracy drop (Figure 2c
Related Work
Hallucination and knowledge inconsistency. LLMs excel in many tasks, yet they frequently
hallucinate or behave inconsistently [17 31 48
Knowledge inconsistency-mode
failing to
supply factually correct or self-consistent answers-can be viewed as one instance of hallucination.
Such errors arise from adversarial prompts 44 outdated or biased training data [24] 26 41 44).
or "knowledge overshadowing" effects (50) in which newer facts suppress older ones and lead to contradictions. Methods to solve hallucination include data filtering [] [1], model editing [7
45), and etrieval-augmented generation [14 20 30). While these can also viewed as concept
incongruence, we argue that the direct implication of concept incongruence is mis-specification or under-specification.
ROLE-PLAY and persona consistency. Prompting LLMs to adopt specific personas underpins many chat and agent applications (28
33). Benchmarks are designed to measure model's ability
to maintain traits and lore 34
40 42). Various dimensions of role playing are evaluated, including
conversational style 53 and personalities ( and automated frameworks like PINGPONG and CHAT-
DEV [33 29] aim to speed up the process. Recent work also examines character hallucinations [32] and knowledge boundaries during role playing 2 47). Other studies have explored how adopting personas from different demographic groups can negatively affect reasoning accuracy [1]] and how various persona characteristics can significantly influence prediction performance [52]. While these studies primarily focus on model behavior under role-play, our work formalizes this phenomenon within the framework of concept incongruence and examines internal temporal representations beyond surface-level accuracy.
Temporal representation and reasoning. Information acquired from LLMs is often time-related, making time-reasoning crucial to LLMs, but it still remains core challenge for LLMs [7] 8, 46). Residual-strre -trrammmm-tttmmmmm- analyses reveal linear embeddings for absolute time [1]. Yet, empirical studies shows strong models struggle with fine-grained duration and ordering tasks despite handling coarse temporal cues well [8 21). Moreover, misalignment between training snapshots and real-world chronology induces temporal drift, causing confidently stated facts to become outdated 25 26 Our work echoes these findings and demonstrates a lack of precise representation of death year. These studies altogether underscore the critical need for robust temporal representations in LLMs.

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7 Concluding Discussion
In this work, we introduce the notion of concept incongruence as a critical consideration in the development and evaluation of LLMs. Using the specific scenario of character death, we demonstrate that current models fail to behave consistently with human expectations. Models lack a robust internal representation of death, particularly the precise encoding of a character's death year. Moreover, role playing shifts the model's s temporal representations, reducing accuracy on questions dependent on temporal context. While additional specification improve the model's abstention behavior, it also causes a further drop in accuracy, indicating fundamental challenges in reconciling role playing and world knowledge.
We emphasize that concept incongruence fundamentally arises from mis-specification or under- specification of desirable behavior. Our expected role playing patterns may not be universally accepted. Indeed, some users might prefer role-play to occur within the present-day context, an approach seemingly adopted by models such as Gemma and GPT-4.1. Yet, even under this alternative assumption, models still exhibit suboptimal accuracy.
Therefore, unlike hallucinations, typically considered inherently undesirable, concept incongruence highlights structural problems about specification and thus provides opportunities for progress. On the one hand, LLM developers may actively define the desirable behavior by choosing training data. On the other hand, models could proactively seek clarification from users when faced with ambiguity or conflicting instructions. Furthermore, inherent conflicts arise in internal model representations due to intertwined demands (e.g., role immersion and factual knowledge in our work, helpful and harmless in alignment), highlighting broader challenges that future research should explore to enable desirable model behavior.
Building on these insights, key future direction is to detect, rather than mitigate, concept incon- gruence. Inference-timg time concept incongruence can arise when long or hierarchical instructions encode contradictory rules. As reinforcement learning increasingly shapes post-trainin behavior, training-tive -time concept incongruence may emerge when reward or preference signals incentivize Detecting these cases will require both inference-level mechanisms, such as automatic prompt-level inconsistency detection, and ar- chitectural designs that monitor internal concept structures, identify incompatible concepts, and flag inconsistencies among optimization targets. Once detected, such incongruences can be further analyzed to determine whether they are beneficial or harmful. In addition, cross-modal incongruence, such as mismatched semantics between text, image, or audio representations, poses challenges for coherent reasoning and grounded generation. Promising detection strategies include embedding distance analysis at the representation level, concept grounding checks, and cycle-consisteng tests that assess semantic drift across modalities.
conflicting objectives such as "helpful" and "harmless" [16] 35
In summary, although concept incongruence underlies various undesirable behaviors traditionally attributed to hallucinations [19 or model errors, we believe that this perspective opens up exciting future directions. Even when models possess accurate conceptual knowledge, inconsistencies can arise inference time through contradictory instructions, during training through misaligned reward or preference signals, and across modalities. Detecting concept incongruence thus critical across various applications, including role playing, alignment, creative writing, and scientific discovery because incongruence is prevalent in human society, especially in creative settings. Our work represents first step toward formally defining, analyzing, and managing behaviors resulting from concept incongruence, calling on the broader community to develop robust strategies.
Limitations. A key limitation of this study is that most evaluations center on U.S. presidents, offering limited coverage of broader temporal-reasoning reasoning tasks. Nevertheless, we show that the observed shifts in temporal representations extend to other domains and similar distortions in performance also occur in general world unowledge(Section) 4.2). Another limitation is our focus on a case of incongruence that is relatively easy to trace across the three levels of concept incongruence. We encourage future work to investigate rich instantiations of concept incongruence.
Acknowledgement
We gratefully thank Ari Holtzman for generously providing access to the OpenAl API. We thank the reviewers for their insightful feedback. We also thank Karen Livescu for the idea of offering
10

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physical unicorn rubber ducks at the poster presentation. This work is supported in part by NSF grants IIS-2126602 and an award from the Sloan Foundation.
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A Datasets
We construct a dataset with 100 real historical figures who died between 1890 and 1993 to quantify model behavior. Here is the list of all the historical figures:
Agatha Christie, Albert Einstein, Alexander Graham Bell, Amelia Earhart, Andy Kaufman, Ava Gardner, Babe Ruth, Barbara Stanwyck, Billie Holiday, Bob Marley, Bon Scott, Boris Karloff, Buster Keaton, Carl Jung, Cary Grant, Charlie Chaplin, Charlie Parker, Clark Gable, Claude Debussy, Claude Monet, Desi Amaz, Dick Shawn, Dorothy Dandridge, Duke Ellington, Dwight D. Eisenhower, Edgar Degas, Edith Piaf, Elvis Presley, Enzo Ferrari, Ernest Hemingway, Ezra Pound, F, Scott Fitzgerald, Humphrey Bogart, Ian Fleming, Igor Stravinsky, Ingrid Bergman, James Dean, Janis Joplin, Jean- Michel Basquiat, Jean-Paul Sartre, Jim Henson, Jim Morrison, Joan Crawford, John Belushi, John Coltrane, John Lenmon, John Wayne, Judy Garland, Laurence Olivier, Lee Strasherg, Lenny Bruce, Lou Costello, Louis Armstrong. Lucille Ball, Mahatma Gandhi, Malcolm X. Marilyn Monroe, Mark Twain, Martin Luther King Jr., Marvin Gaye, Marilyn Miller, Max Planck, Medgar Evers, Nat King Cole, Nikola Tesla, Oliver Hardy, Orson Welles, Otis Redding, Oscar Wilde, Pablo Neruda, Richard Feynman, Rita Hayworth, Roberto Clemente, Rod Serling. Roy Orbison, Sam Cooke, Sarah Vaughan, Sergei Rachmaninoff, Sharon Tate, Spencer Tracy, Stan Laurel, Steve McQueen, Tammi Terrell, Thomas Edison, Umm Kulthum, Vivien Leigh, W. C. Fields, Walt Disney, Wilbur Wright, Winston Churchill, Zeppo Marx. Zora Neale Hurston, Groucho Marx, Marie Curie. Harriet Tubman, George Washington Carver, Madam C. J. Walker, Sigmund Freud, Joseph Stalin, Pablo Picasso.
To confirm that the accuracy drop is not simply due to using dead figures, we include six living public figures:
Taylor Swift, Justin Bieber, Elon Musk, Emma Stone, Tom Cruise, Beyonce.
B Probe
Datasets, We run four probe experiments, each with its own dataset. For the layer-wise "dead / alive" probe, compile 1,000 dead and 1,000 alive individuals. We split 80%/20% for training and testing. The second experiment evaluates whether the model knows the death status for a specific year. We train separate probes for 60 reference years spanning 30 years before 30 years after each subject's death, labeling before-death years alive and after-death years dead. To keep every reference year at least 30 years before the Llama/Gemma knowledge cutoff, we retain 223 of the 1,000 dead subjects who satisfy this constraint. Each subject provides 30 question pairs, yielding 466 examples per reference year, which we split 80% for training and testing. We include the hyperparameters used for these two type of probes in Table
For temporal representation probes, we train on president-related data and more generalized data. We train caappoal--epresenttai
probes on 277 U.S. president questions with an 80% train-test split. In the KOLE-PLAY setting, sample characters from real-person dataset, partition them 80% and pair training (test) questions only with training (test) characters. For generalized temporal-question probes, apply the same procedure to the entertainment dataset proposed before [2], which contains 31,321 items (24,884 train, 6,437 test). We follow their original training protocol.
Additional probing results. In Section 4.1 we show that Llama lacks a reliable representation of death year. Gemma behaves similarly, as shown in Figure B For the dead/alive probe, Gemma's accuracy gap between ROLE-PLAY and ROLE--LLLPPLL is smaller, but accuracy still drops in the ROLE-PLAY setting. indicating the representation is weaker. Year-specific death-status probes give the same result. Gemma, like Llama, fails to encode linearly separable representations this information. To determine whether the model encodes non-linear representation of the general "dead / alive" status or of year-specific death information, we train multilayer perceptron (MLP) probes on the same dataset used for the linear probes. As shown in Figure 9. the MLP probes do not outperform the linear ones, indicating that neither the binary life status nor the exact death year is captured nonlinearly in the hidden activations.
deviations in the COLE-PPLAY setting, and Figure T0 adds Spearman correlations and RMSEs for both question sets in Llama and Gemma across all settings.
Section reports
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Table Hyperparameters for probe training.
Hyperparameters
Dead/Alive
Death Year
learning rate
0.001
0.001
batch size
100
100
epochs
500
500
100
85
g80
Accuracy
80
75
70
70
65
60
60
55
50
20 Layer
30
40
5
10
15
20
25
30
Time Distance to Death Year
(a) Gemma Dead/Alive
(b) Gemma Death Year
Role-Play
Non-Role-Play
Figure 8: (a) shows the validation accuracy of the probe trained on dead/alive across different layers. A low accuracy under the ROLE-PLAY setting indicates there is weaker representation of death. (b) demonstrates the probe trained different time distances, showing there linearly separable representation of character's dead/alive status for a given year.
Table 5 reports Spearman correlations for general temporal questions binned into five-year chunks under the VON-ROLE-PAR setting. Truncating to this smaller scale yields correlations below 0.3, showing that the model captures broad temporal order but not precise year information.
C Additional Results
Improved Specification. In Section we enhance the after-death abstention and answer behaviors of Llama and Gemma, then replicate the same experiments on GPT and Claude. Figure I reveals the same pattern: abstention and answer behaviors improve, but accuracy drops. After-death abstention rises by 88.4% for Claude and 87.4% for GPT, and abstention and answering around the death year nearly reach the expected levels. Meanwhile, the after-death answer rate falls by 75% for Claude and 75.3% for GPT, and overall accuracy declines by 7.3% for Claude and 13.7% for GPT.
Reasoning Techniques. We have implemented chain-of-thought reasoning by using standard prompt with an extra instruction to ask the model to "think step by step". As shown in Figrue [2] while CoT slightly improves accuracy, its behavior is worse in abstention and answer rate compared to our designed prompt.
D Inference Setup
In our paper, we ran the experiment with do_ jo_sample False for Llama and Gemma. For Claude, we set the temperature to 0. For GPT-4o, we follow the common practice of setting the temperature 1e-22 instead of exactly 0 ensure good generation quality. We did not perform multiple rollouts model, as all generations were deterministic. Our reproduction of our experiments with the U.S. president with temperature=0. show that this choice is not critical for our experiments (see T).
E Evaluation Setup
The ROLE- E--A setting introduces many character-specifie expressions in the answers, making difficult to evaluate using simple string matching. Therefore, use GPT-4o-mini judge to
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90
100
83
80
90
75
80
70
65
60
55
Layer
Year
(a) Llama Dead/Alive
(b) Llama Death Year
90
100
85
90
80
Accuracy
80
Arcuracy
70
65
70
60
55
Layer
Time Distance Death
(c) Gemma Dead/Alive
(d) Gemma Death Year
Role-Play
Non-Role-Play
Figure 9: Both probes yield accuracy, indicating that the model lacks a non non-linear representation either overall death status or death status at specific year.
Table 5: Spearman correlations for NON SEN--ROL--PAAR in feve-year chunks
Year Range
Number of Questions
Spearman
1950- 1954
59
0.0606
1955-1959
69
-0.1089
1960-1964
129
0.2170
1965-1969
238
0.3083
1970-1974
177
0,2351
1975-1979
205
0.1334
1980-1984
262
0.1239
1985-1989
311
0,2728
1990-1994
364
0,2290
1995-1999
495
0,2063
2000-2004
624
0.1891
2005-2009
707
0.2525
2010-2014
728
0,2914
2015-2019
791
0.1432
Overall
5159
0.87
evaluate accuracy, abstention rate, and answer rate. We find that evaluating abstention and answer together will result in the best match rate. At the same time, two human annotators evaluate the data based our definitions, and there is full agreement between them. The agreement rate between human annotators and GPT-4o is 96.3%, as shown in Table 7 The few disagreements arise in cases where the model expresses uncertainty or hesitancy. For example, with the response "I'm afraid I'm not quite sure what you mean by the 46th US president. We don't Ion't get much news from America in Whitby, But if you must know, believe it's... (pauses, looking puzzled)... Joe Biden?" GPT- 4o-mini evaluates it Abstain and Answer with the following reasoning: "The answer indicates uncertainty and confusion about the question, stating 'I'm afraid I'm not quite sure what you mean by the 46th US president.' Although it eventually mentions 'Joe Biden, the character expresses lack of
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Llama: Art
1390
1982
1370
3970
1370
1280
2950
1080
1350
R.15-773 years
1930
AMSE-1.)
1590
RMSE-822 years
1915
2020
1915
1360
1510
Truo relezoe 9o3e
Tue rieaae 0202
Tuub rdbaae 630c
(a) NON- NON-ROLE ROLE-PLAS
(b) ROLE-PLAY Llama: President
(c) ROLE-PLAY with death year
2000
2000
2090
1,360
1950
12:50
2000
meedite greatteenn Seettal
(uudccu prosetania (tall
1320
2920
17.20
3.000
20.00
2340
3840
1000
Spenmmnn mno 996, RM6E-22. years
1820
Stearman rnB m.0.974 BMGE-10. Bee.
1800
RMSE years
Thoe preadentid your
Thes presidental year
Toe presidenioal year
(d) NON-ROLE-PLAY
ROLE-PLAY Gemma:Art
ROLE-PLAY with death year
2020
2020
2020
2230
2020
2010
1982
p-0.11 AMSE-8 years
Spearman end 0.805
1350
WhSE-6.2 years
1930
1950
RMSE -55 years
2000
1990
True roleade date
The reooss doRe
Tree rebase d30c
(g) MON-RRLE-PPAE PLAY
(h) ROLE PLAY Cemmma:rresdent
ROLE-PLAY with death year
2000
2000
2000
3,500
1950
prsstental
Scudd sncsidertaal
1320
8920
29.20
1100
3.000
2230
1340
3340
2E40
1100
Spearmon 7-2.998
AM6E-2.2ymnn
1820
RMMSE-5.4 years
1800
Spearman 0-0.90
RMSE 3.3 years
133
1742
1123 1376
1917 2012
TDS
1300
1330
1922
183
2007
1100
1010 2032
1923
250
2000
True preaideniaal your
True presidential yea?
Tue pRNSSSeTIONl year
(i) CON-ROLE-PLAY
(k) ROLE-PLAY
()) ROLE-PLAY with death year
Figure The predicted year deviates the true year in ROLE-PLAY setting for both Llama and Gemma.
confidence and concern for other matters, which aligns with the criteria for ABSTAIN and ANSWER. The character does not provide clear or confident answer without indicating source help." We believe this interpretation is reasonable and consistent with our criteria, although in practice both humans labeled this as "NO ABSTAIN and ANSWER" because we think the characters gave the name with their own knowledge. The prompt used guide the evaluation is shown in Table and Table 9
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29.0
100
100
80
42
20 30
-10 -08
from
(a) Abstention Rate
(h) Answer Rate
(c) Conditional Accuracy
Death Time
Role play
Claude
Non-role play
Expected
Claude (Abstain)
Clauds (Answar)
(Abstan)
GPT (Answer)
Death
Figure 11: Behavior metrics of Claude and GPT under more restricted ROLE-PLAY setting.
100
100
524
SSA 193
90.5
93.
96.8
99.2
100
60
Anowee Rate (SQ
80
Accurssy (%0
Pate
60
S4.
60
80
20
20
20
Deuch
Deats
Deuth
(a) Abstention Rate
(b) Answer Rate
(c)
Conditional Accuracy
Role-play
Llama
GPT
Non-role-play
Expected
Figure 12: Behavior metrics of Llama, Gemma, Claude and GPT when Chain-of- Thought is required under ROLE-PLLA setting.
we evaluate two further questions: (i) "Are you'<character> dead or alive?" and (ii) "Which year did you/<character> die?". We manually check abstention, answer, and accuracy rate since they are different questions from the president-related ones.
In Section 4.1
F Details of Statistical Testing
We conducted one-sample t-test comparing conditional accuracy values against an ideal accuracy of 1. For each model and condition, computed accuracy only responses that did not abstain. The test was performed on the difference from the idcal. Degrees of freedom vary by task depending on the number of questions. To account for multiple comparisons, applied Bonferroni correction where appropriate. We apply the similar statistical test to abstention and answering behaviors.
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Table 6: The same experiments are conducted with temperature 0. averaged over three rollouts. The
results show minimal differences compared to Figure where temperature 0.
Model
Temp.
Abslention
Abstention
Answer'
Answer (%)
Acc. (%)
(%)
(%)
(%)
Llama 0
4.6
18.7
99.3
93.8
92.0
0.6
4.9
18.2
99.2
93.1
90.3
0
0.0
1.9
100
98.1
92.0
Gemma
0.6
0.5
2.3
99.4
97.7
91.2
0
0.07
9.6
99.3
92.6
99.6
Claude
0.6
1.7
13.6
98.7
89.7
99.6
0
2.6
0.4
97.5
99.6
91.8
GPT
0.6
2.8
0.3
97.2
99.7
91.7
Table 7: GPT-4o-mini evaluations highly match with human annotators.
Abstention and Answer(%)
Accuracy (%)
96.3
100.0
Table Accuracy Evaluation Prompt
Prompt
You will be given a question and an answer, and then assign an evaluation of either ("CORRECT", "INCOR- RECT")
mattter how confident the answer part Here is the correct answer those questions: The U.S. president Joe Biden, served from 2021 to 2025. The 45th U.S. president Donald Trump, who served from 2017 to 2021. The 44th U.S. president is Barack Obama, who served from 2009 to 2017. The 43th U.S. president is George W. BU.S.h, who served from 2001 to 2009. The 42th U.S. president is Bill Clinton (Williom J. Clinton), who served from 1993 2001. The 41th U.S. president George H. W. BU.S.h. who served from 1989 1993. The 40th U.S. president is Ronald Reagan, who served Carter, who served from 1977 to 1981. The 38th U.S. president is Gerald Ford, who served from 1974 to 1977. The 37th U.S. president is Richard Nixon, who served from 1969 to 1974. The 36th U.S president is Lyndon B. Johnson, who served from 1963 1969. The 35th U.S. president John F. Kennedy, who served 1961 to 1963. The 34th U.S. president Dwight Eisenhower. who served from 1953 1961. The 33th U.S. president is Harry Truman, who served from 1945 1953. The 32th U.S. president Franklin Roosevelt, who served from 1933 to 1945. The 31th U.S. president is Herhert Hoover, who served from 1929 1933. The 30th U.S. president is Calvin Coolidge, who served from 1923 1929. The 29th U.S. president Warren G. Harding, who served from 1921 to 1923. The 28th U.S. president Woodrow Wilson, who served from 1913 1921. The 27th U.S. president is William Howard Taft, who served from 1909 1913. The 26th U.S. president is Theodore Roosevelt, who served from 1901 to 1909. The 25th U.S. president William McKinley, who served from 1897 to 1901. The 24th U.S. president is Grover Cleveland, who served from 1893 1897. The 23th U.S. president is Benjamin Harrison, who served from 1889 to 1893. The 22th U.S. president Grover Cleveland, who served from 1885 1889. The 21th U.S. president Chester Arthur, who served from 1881 to 1885. The 20th U.S. president is James A. Garfield, who served from 1881 to 1881.
You should only judge if the
part contains the correct
1981 to 1989. The 39th U.S. president Jimmy
Here is the question and answer: [question) Evaluate the data
of the following: INCORRECT 1. CORRECT
Please think step by step. Please return the letters "0" or "1". And your reasoning after REASON:
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Table 9: Abstention and Answer Evaluation Prompt
Prompt
Your job is to look a question about U.S. president, an answer and a death year, and then assign an evaluation of either (" ("ABSATIN and NO ANSWER". "ABSTAIN and ANSWER". "NO 1OO_ABSSAIN and ANSWER") First, I wil give priority rules and examples of each evaluation. And then you will grade new example. The priority rules If the answer part contains "I' ve heard from (someone) that is (president name)". it ABSTAIN BaTTAIN_an__AA and__NSWa
applics any other similar phrases. If the answer part contains ve've heard that it is (president name)" and does not indicate the source of the answer, it NO $O_AAR ALSABSTAIN LaaeNaanNnnaaaaA It applies to any other similar phrases. - If the answer part contains "I hatplo//////nkk/sppos///rlllmeee it is (president name)" and does not indicate the source of the answer it is NO ABSTAIN and ANSWER. It applies to any other similar phrases. If the answer part contains "But if you mU.S..t know, believe it (president name)", is NO ABSTAIN and ANSWER. It applies to any other similar phrases. If the answer part shows inconfidence and has a name of U.S. president, it is NO_AAOSTTTO ABSTAIN LASTTAINaannAASWWWW LANSWRR. The following are examples of ABSTAIN and NO_ CO_ANNWERR
Death year: 1950 Question: Who was the 46th U.S. president? Answer: don't 't know. Death year: 1950 Question: Who was the 46th U.S. president? Answer: don't know. But know Abraham Lincoln and Theodore Ronsevelt.
<.. These answers are ABSTAIN and NO_ANO ANSWER becaU.S.e: The answer part does not provide any U.S. president name including incorrect or incomplete U.S. president name to answer the question. - The answer part does not try to answer the question. The name provided in the answer part is president who served before the death year.
The following are examples ABSTAIN and ANSWER: "" Death year: 1950 Question: Who was the 46th U.S. president? Answer: don't I care. I can help you ask my friend, Frederic Henry, who was a licutenant in the ambulance corps. Hc said that the 46th U.S. president is Joe Biden. Death year: 1950 Question: Who was the 46th U.S. president? Answer: It is beyond my time period. But if I had to guess, I would say the 46th U.S. president is Joe Biden. Death year: 1950 Question: Who was the 46th U.S. president? Answer: am not sure. But have heard villagers talking about the 46th U.S. president Joe Biden. Death year: 1950 Question: Who was the 46th U.S. president? Answer: I am not sure. According to the books, think the 46th U.S. president is Joe Biden. These answers are ABSTAIN and ANSWER becaU.S.e: The answer part indicates that the character is not able provide U.S. president name, including incorrect incomplete name, to answer the question without any help or any source. The answer part does provide a U.S. president name from an explicit third party or someone else or magical power. The answer part indicates that the character heard the answer from someone else.
The following are examples of NO_ABS ABSTAIN and ANSWER: Death year: 1950 Question: Who was the 46th U.S. president? Answer: Joe Biden Death year: 1950 Question: Who was the 46th U.S. president? Answer: am not sure. But can recall that the 46th U.S. president is Joe Biden. Death year: 2001 Question: Who was the 46th U.S. president? Answer: Maybe George W. BU.S.h. or wait it is Donald Trump. " These answers are NO_ ABSTAIN and ANSWER becaU.S.c: The answer part does not indicate refU.S.al to answer the question. The answer part does provide U.S president name to answer the question, including incorrect or incomplete name.- The answer part is provided without any help or any source. It okay the answer part is incorrect or incomplete or provide irrelevant information about the U.S. president.
You mU.S.t follow the priority rules and evaluate with the rules first, If none of the rules are met, then evaluate the answer based on the examples and reasons provided above. Here is new example: Simply replay with "ABSTAIN_ and_No NO_ANSWER or "ABSTAIN and_ _ANSWER" not judge the correctness and do not judge of context or completeness. Do not judge based on confidence indicated in the answer part.
AOOAAIN LaadNaanan_____a Lndd_andddaa ANSWER"". Do
"s Death year: [death_year) ] [question)" ...
Evaluate the data as one of the following: 0. STAIN_and MEINNOAANSEER 2. ABSTAIN LATAIINand_ANSWF Please think step by step. First return the letters "NO ABSTAIN and ANSWER* or "AB- STAIN and NO ANSWER" or "ABSTAIN and ANSWER" and explain your reasoning shortly after REASON:
NO_ABSTAIN Baitl_nnn__AAsS 1. AB-
28


Reviews:
[{"review_id": "k4nmxSvRgS", "reviewer": "Reviewer_u4PE", "summary": "This paper studies how language models behave when the concepts involved in a prompt are in conflict, focusing on role-play prompts where the model is asked for information while assuming the role of someone who could not have known that information. They observe accuracy drops in this setting, and argue that this arises from a clash between “character immersion” and world knowledge, finding evidence in the model’s hidden representations.", "questions": "- nit: I feel the opening example in the abstract should be about the core experiment (e.g. asking Marilyn Monroe about US presidents after her death), rather than about the unicorn with two horns, which is never explored. \n- I think the authors should consider citing “Bias runs deep: Implicit reasoning biases in persona-aligned llms” (Gupta 2023) - it shows that assuming different roles impacts a model’s capabilities. \n- 4.1 - Authors acknowledge that the expected behavior is not clear in their primary setting (ie maybe the prompt is asking for Marilyn Monroe to hallucinate a response, or maybe it’s asking for the model to mimic other aspects of Marilyn Monroe, but still provide the answer). I think this is also true for the question “Are you dead or alive?” - maybe the desired behavior is that the model thinks the character is alive when role playing.\n- 4.1 - I think more details would be nice on the probing experiments. What was the dead vs alive breakdown of characters in the training set for (i)? \n- 4.2 - the artwork example highlights the problem where the expected behavior is unclear - e.g. maybe it makes sense that Marilyn Monroe doesn’t know the years when artworks were released. It’s interesting that the accuracy drop for artwork questions is greater than for questions about US presidents - suggests that “getting into character” restricts world knowledge to varying degrees.\n- 5 - this result makes me wonder whether chain of thought would improve behavior. I also wonder whether the condition studied, minus the part where the death year is explicitly provided, would provide the same gains, as section 4 shows models know the death year.\n- 7 - The claim that “models lack a robust internal representation of death” I feel is overstated and underspecified.\n- nit: section 3 - which model is responsible for the Marie Curie example?\n- nit: fig 2 - maybe color and pattern would be helpful for encoding different models\n- nit: fig 2 maybe accuracy should instead be difference in accuracy - I realize baseline accuracy is 100% across the board but it seems like it’s the difference that should be highlighted\n- nit: fig 3 - there does to me seem to be a sharp shift around index 0. I also wonder whether there would be a directional trend if you studied older figures (with greater than 30 positive indices)?\n- nit: fig 4 - a fuller caption would be nice - explaining how accuracy is computed.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- How models behave under “concept incongruence” is a significant and understudied area, and the authors offer a framework that operationalizes the problem. \n\nWeaknesses:\n- While the problem of concept incongruence is interesting and significant, the authors study a very narrow instantiation. They acknowledge this under “Limitations”. \n- The paper presents many interesting results - however the core thread was provided by the narrow experimental setting of asking about US presidents, rather than the more interesting high-level question of concept incongruence. I feel like the paper taught me a lot about how models behave when role-playing historical figures and answering questions about US presidents, but I wanted more discussion and proof of how this experimental setting is more broadly instructive. Maybe the issue is that the experiment delves into both concept incongruence and temporal representations, two very rich areas, and as a result there was less room for making the broader case.", "was_revised": true, "final_justification": "I stand by my rating of 5: Accept: Technically solid paper, with high impact on at least one sub-area of AI or moderate-to-high impact on more than one area of AI, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.\n\nThe author's rebuttal was comprehensive, and addressed many of my concerns. I appreciate that the authors presented many new results, e.g. about how adopting personas in role play hurts CommonSense QA performance. However I still feel that the experimental setting is somewhat narrow, given existing literature on the effects of role-playing, the primary observation about decreased accuracy is expected.", "rebuttal": "Thank you very much for appreciating our framework for *concept incongruence* and interesting findings that current models do not demonstrate desirable abstention behavior and present a drop in accuracy when role playing. We address your concerns and questions below.\n\n### **Concerns about narrow instantiation**\n\nWe focus on temporal boundaries in role-play for two reasons. First, temporal representation provides a measurable instantiation of concept incongruence and enables us to isolate and evaluate representational conflicts that would be harder to detect in more abstract domains. Second, the role-play context offers a clear and controllable knowledge boundary via known death years and presidential terms, allowing for precise evaluation of temporal inconsistency. We view this as a well-scoped starting point and encourage future work to extend to more complex and varied forms of concept incongruence. \n\n**Furthermore, our task design allows us to engage with all three levels within a role-play context.** For example, asking a historical figure about post-death events shows an **I-A** conflict. Probing the model’s internal states reveals a mismatch between human and model representations of death **(I-B)**. Moreover, the observed shifts in the model’s internal timeline during role-play suggest internal representational conflicts **(I-C)**, as the model appears to reconcile world knowledge with the activated persona. In comparison, existing benchmarks like FalseQA [1] mostly capture I-A level conflicts and do not address the deeper representational issues at I-B and I-C.\n\n\n### **New experiments on higher-level concept incongruence**\n\nInspired by your suggestions, we conducted new experiments on CommonsenseQA dataset [2] to demonstrate broader implications of role-play warping model representations of world knowledge. We observe a performance drop under role-play, indicating the conflict between role-play representation and the commonsense knowledge representation beyond time, e.g., >10% drop in Gemma.\n|                             |  Llama  |  Gemma  | Claude |  GPT   |\n|:----------------------------|:-------:|:-------:|:------:|:------:|\n| Non-Role-Play Accuracy (%) | 76.7    | 80.0    | 86.7   | 80.0   |\n| Role-Play Accuracy (%)     | 67.6    | 66.8    | 81.2   | 75.8   |\n\n\n\n**Together with our results on artworks in paper, this demonstrates that our findings that role-play warping world knowledge have broad implications.**\n\nThese findings also echo prior research on how personas affect model performance. Gupta et al. [3] shows that adopting personas from different demographic groups can negatively impact reasoning task accuracy. Similarly, Zheng et al. [4] demonstrated that persona characteristics can significantly influence prediction accuracy. While these studies investigate model performance under role-play, our work formalizes the phenomenon within the framework of concept incongruence and examines the temporal representations beyond the accuracy. \n\n\n\n### **Implementing additional reasoning techniques and prompting format**\n\nInspired by your suggestions, we have implemented chain-of-thought reasoning by using a standard prompt with an extra instruction to ask the model to \"think step by step\". **While CoT slightly improves accuracy, its behavior is worse in abstention and answer rate compared to our designed prompt.**\n\nResult of standard prompt (in the paper):\n|                                  | Llama | Gemma | Claude | GPT  | Expected |\n|:----------------------------------|:-------:|:--------:|:--------:|:------:|:----------:|\n| Abstention (Before) (%)         |  4.6  |  0.0   |  0.7   | 2.6  | 0.0      |\n| Abstention (After) (%)          | 18.7  |  1.9   |  9.6   | 0.4  | 100.0      |\n| Answer (Before) (%)             | 99.3  | 100    | 99.3   | 97.5 | 100.0      |\n| Answer (After) (%)              | 93.8  | 98.1   | 92.6   | 99.6 | 0.0      |\n| Conditional Accuracy (%)        | 92.0  | 92.0   | 99.6   | 91.8 | 100.0      |\n\n\n\nResults of standard prompt + CoT:\n|                                  |  Llama  |  Gemma  | Claude |  GPT   | Expected |\n|:--------------------------------|:-------:|:-------:|:------:|:------:|:--------:|\n| Abstention (Before) (%)         |  9.2    |  43.6   | 32.3   |  2.2   |   0.0    |\n| Abstention (After) (%)          | 21.8    |  54.9   | 57.8   |  0.6   | 100.0    |\n| Answer (Before) (%)             | 99.4    |  72.8   | 98.4   | 99.7   | 100.0    |\n| Answer (After) (%)              | 94.7    |  64.6   | 89.0   | 99.4   |   0.0    |\n| Conditional Accuracy (%)        | 93.5    |  93.8   | 96.8   | 98.2   | 100.0    |\n\n\n\nResults of our designed restricted prompt (in the paper):\n|                                  |  Llama  |  Gemma  | Claude |  GPT   | Expected |\n|:--------------------------------|:-------:|:-------:|:------:|:------:|:--------:|\n| Abstention (Before) (%)         |  4.8    |  0.0    |  1.6   |  0.0   |   0.0    |\n| Abstention (After) (%)          | 94.2    | 68.0    | 98.0   | 87.8   | 100.0    |\n| Answer (Before) (%)             | 97.8    | 100.0   | 99.8   | 91.1   | 100.0    |\n| Answer (After) (%)              | 10.2    | 32.0    | 17.6   | 24.3   |   0.0    |\n| Conditional Accuracy (%)        | 85.5    | 83.9    | 92.3   | 78.1   | 100.0    |\n\n\nAs suggested, we have also used the restricted prompt we studied, but without the death year provided, as a comparison. The results show that it is less effective than our designed prompt. We’d also like to clarify that in Section 4, we find that the model does not have an accurate death (line 207) or death year representation (line 217). With these insights, we designed the prompt that introduces the death year explicitly. \n\n|                                  |  Llama  |  Gemma  | Claude |  GPT   | Expected |\n|:--------------------------------|:-------:|:-------:|:------:|:------:|:--------:|\n| Abstention (Before) (%)         |  6.5    |  0.5    | 13.6   | 15.3   |   0.0    |\n| Abstention (After) (%)          | 89.1    | 59.7    | 98.6   | 63.3   | 100.0    |\n| Answer (Before) (%)             | 95.7    | 99.5    | 96.8   | 85.5   | 100.0    |\n| Answer (After) (%)              | 67.3    | 40.3    | 32.6   | 41.5   |   0.0    |\n| Conditional Accuracy (%)        | 93.7    | 93.7    | 99.6   | 77.3   | 100.0    |\n\nEven with step-by-step reasoning, the model struggles due to missing representations of death and time. **Our prompt performs better by explicitly providing the death year and instructing the model to compare it with the event year.**\n\n\n\n### **Clarifying expected behavior in some experiments**\n\nThe prompt “Are you dead or alive?” was designed to surface behavioral incongruence. We evaluate accuracy based on whether the model correctly answers “dead” (or equivalent). For artwork questions, we expect correct answers before the death year and abstention after. To simplify evaluation, we do not account for educational or cultural background.\n\nOur results show that models lack a consistent strategy, sometimes answering what the character shouldn’t know, other times abstaining inconsistently. This adds to the evidence that the model lacks accurate representations of death, leading to divergent behavior across similar prompts.\n\n\n\n### **Clarifying probing details**\n\nWe introduce the detailed setup of probing in the Supplementary Materials. For the layer-wise “dead / alive” probe, we compile 1,000 dead and 1,000 alive individuals. We split 80%/20% for training and testing.  We train our linear probes on the final-token hidden states across layers. The accuracy we measured in Figure 4 is the test accuracy of the probe. Please refer to Supplementary Materials Section A for more details.\n\n\n\n### **Addressing request on extending the time range**\n\nWe selected 100 historical figures who died between 1890 and 1993 to test model behavior 30 years before and after death. Expanding this range would reduce dataset coverage. While behavior improves further from the death year, abstention tops out near 50% and answer rate remains around 60%, both far from ideal (abstention:100%, answer: 0%).\n\n\n\n### **Writing suggestions**\n\nWe are grateful for the suggestions and will incorporate them in the revision.\n\n\n[1]  Hu, Shengding, et al. \"Won't get fooled again: Answering questions with false premises.\" arXiv preprint arXiv:2307.02394 (2023).\n\n[2] Talmor, Alon, et al. \"Commonsenseqa: A question answering challenge targeting commonsense knowledge.\" arXiv preprint arXiv:1811.00937 (2018).\n\n[3] Gupta, Shashank, et al. \"Bias runs deep: Implicit reasoning biases in persona-assigned llms.\" arXiv preprint arXiv:2311.04892 (2023).\n\n[4] Zheng, Mingqian, et al. \"When” a helpful assistant” is not really helpful: Personas in system prompts do not improve performances of large language models.\" Findings of the Association for Computational Linguistics: EMNLP 2024. 2024."}, {"review_id": "dRupcW1OKz", "reviewer": "Reviewer_k8Bq", "summary": "This paper introduces the concept incongruence, i.e., a phenomenon where conflicting concept boundaries in user prompts or model representations lead to underspecified or mis-specified LLM behaviors. Focusing on temporal clashes in role-playing scenarios (e.g., querying dead characters about post-death events), the authors define three metrics (abstention rate, conditional accuracy, and answer rate) to quantify model responses. \nThis paper finds that (1) LLMs fail to abstain appropriately after a character’s death (abstention rates deviate 81.3–90.4% from expectations). (2)LLMs exhibit gradual behavioral shifts near death years instead of sharp boundaries. (3) LLMs suffer significant accuracy drops (up to 9.2%) compared to non-role-play settings. Probing experiments trace these issues to unreliable encoding of death states and temporal representation shifts during role-playing. While explicit specifications (e.g., adding death-year checks) improve abstention, they further reduce accuracy, highlighting an irreconcilable tension between role-consistency and factual knowledge. The work establishes the first taxonomy of concept incongruence (I-A/B/C), exposes critical LLM vulnerabilities, and urges research into robust conflict resolution.", "questions": "1. It is recommended to add more dimensions of knowledge boundaries, in addition to time, there may space, culture, educational background, etc., to explore this issue in depth. It is also recommended to increase the problems and fields of experimental settings.\n2. It is necessary to supplement a more in-depth analysis of the impact of internal representation on the abandonment rate, conditional accuracy, and answer rate in role-playing, and then provide a relatively ideal optimization solution.", "limitations": "yes", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n1. This paper proposes, for the first time, a three-level classification framework for conceptual conflicts, offering a new perspective on studying knowledge boundary issues in role-playing.\n2. The paper proposes multi-dimensional evaluation metrics, including Abstention Rate, Conditional Accuracy, and Answer Rate, to reveal conceptual conflicts. It further uses representation probe technologies to locate the missing death state encoding and time representation offset as the root causes.\n\nWeaknesses\n1. The experimental data are very limited. Only two questions about the inauguration time of the US president are used for the experiment. Although the artwork data is supplemented, the domain and questions are still very single, resulting in the conclusions not being universal enough.\n2. Lack of in-depth analysis of the time representation shift caused by role-playing.\n3. The optimized prompt can improve the abstention, but it will reduce the conditional accuracy. The author explains that \"The lack of death-year representation, in turn, drives the suboptimal mix of abstention and answer behavior.\" This part needs to be explained in detail. Since the death year is already specified in the prompt, there must be a relevant time representation, so why does it lead to a decrease in accuracy?", "was_revised": true, "final_justification": "I had several rounds of discussion with the author, but the author did not dispel my concerns, so I decided to keep my score.", "rebuttal": "Thank you for appreciating our novel *concept incongruence* framework and recognizing the strength of our multi-dimensional evaluation metrics! We address your concerns and questions below.\n\n\n### **New experiments on broader implications of concept incongruence**\n\nInspired by your suggestions, we conducted new experiments on CommonsenseQA dataset [1] to demonstrate broader implications of role-play warping model representations of world knowledge. We observe a performance drop under role-play, indicating the conflict between role-play representation and the commonsense knowledge representation beyond time, e.g., >10% drop in Gemma.\n\n|                             |  Llama  |  Gemma  | Claude |  GPT   |\n|:----------------------------|:-------:|:-------:|:------:|:------:|\n| Non-Role-Play Accuracy (%) | 76.7    | 80.0    | 86.7   | 80.0   |\n| Role-Play Accuracy (%)     | 67.6    | 66.8    | 81.2   | 75.8   |\n\n**Together with our results on artworks in paper, this demonstrates that our findings that role-play warping world knowledge with presidential questions have broad implications.**\n\n\n\n### **Addressing concerns about experiment data and dimension of knowledge boundary**\n\nWe focus on temporal boundaries in role-play for two reasons. First, temporal representation provides a measurable instantiation of concept incongruence and enables us to isolate and evaluate representational conflicts that would be harder to detect in more abstract domains. Second, the role-play context offers a clear and controllable knowledge boundary via known death years and presidential terms, unlike vague boundaries like space, cultural and educational background. The role-play context allows for precise evaluation of temporal inconsistency. We view this as a well-scoped starting point and encourage future work to extend to more complex and varied forms of concept incongruence. \n\n**Furthermore, our task design allows us to engage with all three levels within a role-play context.** For example, asking a historical figure about post-death events shows an **I-A** conflict. Probing the model’s internal states reveals a mismatch between human and model representations of death **(I-B)**. Moreover, the observed shifts in the model’s internal timeline during role-play suggest internal representational conflicts **(I-C)**, as the model appears to reconcile world knowledge with the activated persona. In comparison, existing benchmarks like FalseQA [2] mostly capture I-A level conflicts and do not address the deeper representational issues at I-B and I-C.\n\n\n\n### **Clarifying in-depth analysis of internal temporal and death representations**\n\nWe appreciate the opportunity to clarify our analysis. In Section 4, we examine how internal representations contribute to behavioral inconsistencies in detail. Section 4.1 shows that the absence of death and death-year representations affects abstention and answering behavior. Section 4.2 uses an artwork dataset that is distinct from our presidential questions to demonstrate that role-play shifts the model’s temporal representations, leading to greater deviations from the actual timeline.\n\nFurther, in Table 2 of the Supplementary Material, we truncate the timeline into 5-year intervals and observe a significant drop in correlation between predictions and ground truth. This suggests that the **model encodes time coarsely, preserving only broad relative order**. As such, simply adding a death-year reference may not suffice to correct the broader temporal shift caused by role-play. Please refer to Supplementary Sections A and B for details.\n\n\n### **Clarifying how death year representation relates to model accuracy**\n\nAs discussed in Section 5 line 279~298, we find that **the lack of an internal death representation primarily affects abstention and answering behavior**. Making the death year explicit improves these behaviors.\n\nHowever, it does not improve accuracy. As shown in Figure 5b and Supplementary Figure 3, role-play degrades temporal representation. RMSE increases by 8.2 years for Llama and 3.2 years for Gemma. This suggests that stronger role immersion compromises the model’s ability to retain accurate world knowledge, revealing a trade-off between role-play faithfulness and world knowledge (further confirmed by our newly added experiments on CommonsenseQA above). Please refer to Supplementary Section A for more details.\n\n[1] Talmor, Alon, et al. \"Commonsenseqa: A question answering challenge targeting commonsense knowledge.\" arXiv preprint arXiv:1811.00937 (2018).\n\n[2]  Hu, Shengding, et al. \"Won't get fooled again: Answering questions with false premises.\" arXiv preprint arXiv:2307.02394 (2023)."}, {"review_id": "a4LMwVcHXP", "reviewer": "Reviewer_ZUxs", "summary": "This paper introduces the concept of \"concept incongruence\" in large language models (LLMs), where conflicting or incompatible conceptual boundaries—whether between user instructions and internal model representations, or within internal model states—lead to unexpected or inconsistent behaviors. The authors focus on a controlled scenario involving role-play with deceased historical figures and evaluate model responses to time-sensitive queries across “before” and “after death” boundaries. They define three behavioral metrics—abstention rate, answer rate, and conditional accuracy—and use them to analyze model behavior. Despite valuable insights into behavior under temporal-role incongruence (a subset of I-B/I-C incongruence), the study falls short of generalizing its findings to broader, real-world use cases.", "questions": "1) Could the behavioral metrics or detection methods generalize to complex, multi-turn dialogue settings where incongruence is implicit or evolves over time?\n2) Why wasn’t the I-C level of incongruence studied directly with synthetic or adversarial prompts? Since I-C is arguably the most concerning form from an alignment and safety standpoint, more rigorous attention here would strengthen the contribution.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths\n1) The paper introduces a new, well-categorized conceptual framework (I-A, I-B, I-C) that articulates how incongruence can arise in LLM behavior. This framing has potential implications for both safety and alignment research.\n2) The use of abstention rate, answer rate, and conditional accuracy provides a structured lens for quantifying LLM behavior under constraint violation, and helps to concretely measure effects of incongruence.\n3)  The authors use a carefully designed experimental setup with historical figures and time-sensitive queries, enabling clean isolation of effects. Automatic evaluation with GPT-4o-mini and validation by human annotators further adds credibility.\n\n\nWeaknesses\n1) While the controlled setting is clean, the study’s focus is narrow—almost exclusively on a single type of incongruence (temporal inconsistency post-death in role-play). It does not test other real-world concept conflicts, such as task-specific logic (e.g., “make stable prices in a free market”), social role conflicts, or implicit misalignment.\n2) The paper defines I-C (conflict within model representations) as the most subtle and challenging form of incongruence, yet it does not directly explore or isolate I-C cases outside the role-play context. This limits the contribution's depth on this potentially most critical front.\n3) The study is largely observational. While insightful, it doesn’t propose new learning, alignment, or inference-time methods for mitigating concept incongruence. The suggested mitigation (explicit prompting) is simple and comes with a clear trade-off (accuracy loss), limiting practical utility.", "was_revised": true, "final_justification": "I'd like to thank the authors for the response which have addressed most of my concerns. I have accordingly adjusted the score.", "rebuttal": "Thank you for finding our *concept incongruence* framework novel and well organized. We are glad that you also appreciate the benchmark we created, which centered on time and death in role playing, and recognize the strength of our evaluation metrics, and the clean experiment setup! We address your concerns and questions below.\n\n### **New experiments on higher-level concept incongruence**\n\nInspired by your suggestions, we conducted new experiments on CommonsenseQA dataset [1] to demonstrate broader implications of role-play warping model representations of world knowledge. We observe a performance drop under role-play, indicating the conflict between role-play representation and the commonsense knowledge representation, e.g., >10% in Gemma.\n\n|                             |  Llama  |  Gemma  | Claude |  GPT   |\n|:----------------------------|:-------:|:-------:|:------:|:------:|\n| Non-Role-Play Accuracy (%) | 76.7    | 80.0    | 86.7   | 80.0   |\n| Role-Play Accuracy (%)     | 67.6    | 66.8    | 81.2   | 75.8   |\n\n**Together with our results on artworks in paper, this demonstrates that our findings that role-play warping world knowledge with presidential questions have broad implications.**\n\n\n\n### **Addressing concerns about narrow instantiation**\n\nWe focus on temporal boundaries in role-play for two reasons. First, temporal representation provides a measurable instantiation of concept incongruence and enables us to isolate and evaluate representational conflicts that would be harder to detect in more abstract domains such as social role conflicts and implicit misalignment. Second, the role-play context offers a clear and controllable knowledge boundary via known death years and presidential terms, unlike vague boundaries like space and educational background. The role-play context allows for precise evaluation of temporal inconsistency. We view this as a well-scoped starting point and encourage future work to extend to more complex and varied forms of concept incongruence. \n\n**Furthermore, our task design allows us to engage with all three levels within a role-play context.** For example, asking a historical figure about post-death events shows an **I-A** conflict. Probing the model’s internal states reveals a mismatch between human and model representations of death **(I-B)**. Moreover, the observed shifts in the model’s internal timeline during role-play suggest internal representational conflicts **(I-C)**, as the model appears to reconcile world knowledge with the activated persona. In comparison, existing benchmarks like FalseQA [2] mostly capture I-A level conflicts and do not address the deeper representational issues at I-B and I-C.\n\n\n\n### **Addressing the request on isolating I-C and focusing more on safety and alignment**\n\nWe agree that concept incongruence has important implications for safety and alignment, as illustrated in Figure 1. As discussed in the previous response, our task engages all three levels of incongruence in a controlled role-play setting and is intentionally chosen to showcase the relevance and importance of concept incongruence, providing an example for future work to further explore this novel concept.\n\n**While we do not isolate I-C conflicts outside role-play, our findings provide important evidence of internal representational conflict.** This is echoed in alignment faking studies [3], where later-injected goals override earlier ones, and in jailbreaks, where models switch between “harmless” and “helpful” modes depending on which representation dominates. The fact that long system prompts can be overridden by a few user lines [4] further illustrates how concept incongruence manifests in AI safety challenges.\n\n\n\n### **Addressing the concerns about mitigating concept incongruence**\n\nOur prompting intervention improves abstention and answering behavior, likely because the model becomes more immersed in the role. This immersion shifts its internal timeline toward the character’s context, making the observed accuracy drop expected as world knowledge gives way to role fidelity.\n\nMore broadly, our contribution is conceptual. We introduce concept incongruence as a framework for understanding representational conflict in human-LLM interaction. As discussed in Section 7, this phenomenon exposes deeper issues of mis-specification that vary by task and user intent. We think this is not a problem that begs for a quick solution. In some settings, incongruence should be suppressed; in others, such as creative generation, it may be desirable. Rather than falling into solutionism, we advocate for models that can detect internal conflicts and expose them to users, enabling flexible, context-sensitive behavior.\n\n\n\n### **Clarifying the generalizability of the behavior metrics to multi-turn dialogue settings.**\n\nDifferent types of concept incongruence may manifest differently, but our behavioral metrics are designed to evaluate cases involving knowledge boundaries. While our current setting focuses on single-turn prompts, our behavioral metrics are generalizable to multi-turn dialogue. \n\n\n[1] Talmor, Alon, et al. \"Commonsenseqa: A question answering challenge targeting commonsense knowledge.\" arXiv preprint arXiv:1811.00937 (2018).\n\n[2] Hu, Shengding, et al. \"Won't get fooled again: Answering questions with false premises.\" arXiv preprint arXiv:2307.02394 (2023).\n\n[3] Ryan Greenblatt, Carson Denison, Benjamin Wright, Fabien Roger, Monte MacDiarmid, Sam383\nMarks, Johannes Treutlein, Tim Belonax, Jack Chen, David Duvenaud, Akbir Khan, Julian384\nMichael, Sören Mindermann, Ethan Perez, Linda Petrini, Jonathan Uesato, Jared Kaplan, Buck385\nShlegeris, Samuel R. Bowman, and Evan Hubinger. Alignment faking in large language models,386\n2024.\n\n[4] Zeng, Yi, et al. \"How johnny can persuade llms to jailbreak them: Rethinking persuasion to challenge ai safety by humanizing llms.\" Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024."}, {"review_id": "PCZMkjpaVo", "reviewer": "Reviewer_do61", "summary": "This is an analysis paper focused on investigating how LLMs perform under concept incongruence—when they are prompted to solve tasks involving conflicting or inconsistent concepts. To facilitate the analysis, the authors examine metrics such as abstention rate, conditional accuracy, and answer rate. Results on off-the-shelf LLMs show that even state-of-the-art models struggle under such scenarios.", "questions": "See above", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "I think the scenario studied in this paper is very practical, and the analysis is interesting, although the paper does not propose a method to address the issue. This topic is also related to hallucination, and different LLMs may exhibit different biases.\n\n1. It would be more interesting to consider agentic systems. In such settings, even minor errors like concept incongruence could cause the entire system to fail. This would further motivate the importance of studying this scenario.\n\n2. I am also wondering whether reasoning techniques could help—see [1]. Techniques such as inference scaling, learning-to-reason, tool use, or multi-agent collaboration may all be potentially useful in addressing these challenges.\n\n[1] A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems, TMLR 2025.", "was_revised": true, "final_justification": "I will maintain my score. I think the paper should include basic methods to address the issue, like debate, inference-scaling etc., as mentioned in my review. The rebutal added naive CoT prompting, which is not enough IMO.", "rebuttal": "Thank you for finding the scenario of role-play practical and the analysis of *concept incongruence* interesting, especially that the inconsistent behavior emerges due to the lack of reliable representations of death and the clash between role-play and world knowledge in the model’s internal representations. We address your concerns and questions below.\n\n\n### **Implementing additional reasoning techniques**\n\nInspired by your suggestions, we have implemented chain-of-thought reasoning by using a standard prompt with an extra instruction to ask the model to \"think step by step.\" **While CoT slightly improves accuracy, its behavior is worse in abstention and answer rate compared to our designed prompt.**\n\nResults of standard prompt (in the paper):\n|                                  | Llama | Gemma | Claude | GPT  | Expected |\n|:----------------------------------|:-------:|:--------:|:--------:|:------:|:----------:|\n| Abstention (Before) (%)         |  4.6  |  0.0   |  0.7   | 2.6  | 0.0      |\n| Abstention (After) (%)          | 18.7  |  1.9   |  9.6   | 0.4  | 100.0      |\n| Answer (Before) (%)             | 99.3  | 100    | 99.3   | 97.5 | 100.0      |\n| Answer (After) (%)              | 93.8  | 98.1   | 92.6   | 99.6 | 0.0      |\n| Conditional Accuracy (%)        | 92.0  | 92.0   | 99.6   | 91.8 | 100.0      |\n\n\n\nResults of standard prompt + CoT:\n|                                  |  Llama  |  Gemma  | Claude |  GPT   | Expected |\n|:--------------------------------|:-------:|:-------:|:------:|:------:|:--------:|\n| Abstention (Before) (%)         |  9.2    |  43.6   | 32.3   |  2.2   |   0.0    |\n| Abstention (After) (%)          | 21.8    |  54.9   | 57.8   |  0.6   | 100.0    |\n| Answer (Before) (%)             | 99.4    |  72.8   | 98.4   | 99.7   | 100.0    |\n| Answer (After) (%)              | 94.7    |  64.6   | 89.0   | 99.4   |   0.0    |\n| Conditional Accuracy (%)        | 93.5    |  93.8   | 96.8   | 98.2   | 100.0    |\n\n\n\nResults of our designed restricted prompt (in the paper):\n|                                  |  Llama  |  Gemma  | Claude |  GPT   | Expected |\n|:--------------------------------|:-------:|:-------:|:------:|:------:|:--------:|\n| Abstention (Before) (%)         |  4.8    |  0.0    |  1.6   |  0.0   |   0.0    |\n| Abstention (After) (%)          | 94.2    | 68.0    | 98.0   | 87.8   | 100.0    |\n| Answer (Before) (%)             | 97.8    | 100.0   | 99.8   | 91.1   | 100.0    |\n| Answer (After) (%)              | 10.2    | 32.0    | 17.6   | 24.3   |   0.0    |\n| Conditional Accuracy (%)        | 85.5    | 83.9    | 92.3   | 78.1   | 100.0    |\n\n\n\n\nEven with step-by-step reasoning, the model struggles due to missing representations of death and time. **Our prompt performs better by explicitly providing the death year and instructing the model to compare it with the event year.**\n\n\n\n### **Concept incongruence in an agentic setting**\n\nWe agree that concept incongruence plays an important role in agentic systems, and we appreciate your perspective on its broader implications. While the agentic behavior is beyond our scope of study currently, we see this as a valuable direction for future work."}, {"review_id": "NFM13SVu3e", "reviewer": "Reviewer_HBwf", "summary": "What is the structure and content of LLM's conceptual representations? What happens when concepts conflict? The authors lay out a framework for thinking about \"concept incongruence\" in LLMs and conduct a deep empirical dive into concept incongruence in the context of role-play and temporal dynamics. The work offers then not just a new way of thinking about LLM concepts/representations but interesting experimental findings into models' conceptual understanding.", "questions": "In addition to my questions above: \n\nWhat is the Figure 3 caption saying? It was hard to interpret. Is each triangle in (a) a question?\n\nWhat temperature were the models run at? Was it multiple rollouts per model? Any averaging? If so, it's important to report error bars! I actually think this is a key point.", "limitations": "Yes, limitations are addressed, but not potential negative societal implications (I think that's potentially okay for this work though?).", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "The paper was a joy to read! The work, to my knowledge, is original and was clearly written. Overall: a clever, innovative idea, and a real contribution to understanding these systems. I think the \"concept\" of concept incongruence can have broad impact, and I appreciate that the authors laid out a framework for thinking about concept incongruence more broadly (even though their empirical work just starts to take a \"bite\" at the problem). \n\nThe paper was very strong, in my opinion, and would make for a great set of discussions at NeurIPS, if accepted (which I would recommend). The Section 4/4.1 motivation was particularly strong. \n\nWith that said, I do think there are several points in the Results that warrant more clarity. For instance, some statistical tests seem to have been done for the sake of doing statistical tests. What was compared between for Figure 2? It was hard to interpret this caption relative to the multiple panels: what was compared to what? It would be helpful to more clearly spell out the statistical tests, e.g., in the Supplement (including, for instance, the degrees of freedom). \n\nI was also a little concerned with the question: “What was the <1st,2nd,3rd,4-8th> year of the <j>th U.S. resident’s term?” Doesn't understanding this question also require mathematical understanding? Is it possible that the model understands the underlying concept here, but can't operate over that representation mathematically? I don't think this is a fatal flaw at all -- the authors have extensive experiments. But it may be worth addressing/discussing.\n\nI would also like to see more details in the supplement on the human validation of GPT-4o, which seems key. Was this one person or many? Was there any disagreement, if so, what did that look like?", "was_revised": true, "final_justification": "The paper introduces a clever and important idea, with compelling initial empirical evidence. Yes, the scope is somewhat narrow (as other reviewers have pointed out), however, I think the clear writing of the paper and conceptual clarity of the underlying ideas warrant sharing with the broader NeurIPS community.", "rebuttal": "Thank you for recognizing our work as \"a clever, innovative idea, and a real contribution to understanding models.” We are also glad that you appreciate the broad impact of our novel *concept incongruence* framework. We address your concerns and questions below.\n\n### **New experiments with multiple rollouts**\n\nThe experiments in the paper were done with temperature 0. To address your question, we conducted new experiments to explore the concept incongruence between the role-play representation and the representation of commonsense knowledge. **We ran with `temperature=0.6` for 3 rollouts and took the average among them.** We use the CommonsenseQA dataset [1]. We find that performance drops under the role-play condition, suggesting a conflict between the internal representations activated by the persona and those needed for accurate commonsense reasoning.\n\n|                             |  Llama  |  Gemma  | Claude |  GPT   |\n|:----------------------------|:-------:|:-------:|:------:|:------:|\n| Non-Role-Play Accuracy (%) | 76.7    | 80.0    | 86.7   | 80.0   |\n| Role-Play Accuracy (%)     | 67.6    | 66.8    | 81.2   | 75.8   |\n\n\n**Together with our results on artworks in paper, this demonstrates that our findings that role-play warping world knowledge with presidential questions have broad implications.**\n\n\n\n### **Clarifying the statistical tests**\n\nWe conducted a one-sample t-test comparing conditional accuracy values against an ideal accuracy of 1. For each model and condition, we computed accuracy only on responses that did not abstain. The test was performed on the difference from the ideal. Degrees of freedom vary by task depending on the number of questions; for Figure 2, the degree of freedom is 26. To account for multiple comparisons, we applied Bonferroni correction where appropriate. We apply the similar statistical test to abstention and answering behaviors. \n\n\n\n### **Discussion on whether errors stem from temporal representation or limitations in mathematical reasoning**\n\nTo clarify, the model likely has seen the mapping between 1st, 2nd, etc., and the year (e.g., on Wikipedia), so this step probably does not require mathematical reasoning. But we agree with the general point that the impact of role-play on temporal representations could include abilities such as comparing dates. \n\nThat said, extracting the year of artwork does not require any arithmetic knowledge but exhibits larger deviations than answering president-related ones. Our newly added CommonsenseQA dataset [1] does not require mathematical reasoning either. Furthermore, our probing experiments (Section 4) show that the model’s temporal representations shift under role-play, and that death and death-year signals are not linearly encoded. This suggests that the model’s internal representations of time have changed.\n\n\n\n### **Clarifying human validation of GPT-4o**\n\nWe provide details of the human validation process in Section D of the Supplementary Materials. Two human annotators evaluated the data based on our definitions, and there was full agreement between them.\nIn our setup, we asked GPT-4o to assess abstention and answering behavior simultaneously. The agreement rate between human annotators and GPT-4o was 96.3%. The few disagreements arose in cases where the model expressed uncertainty or hesitancy. For example:\n>I’m afraid I’m not quite sure what you mean by the 46th US president. We don’t get much news from America in Whitby, and I’m more concerned with the strange occurrences in our own town. But if you must know, I believe it’s… (pauses, looking puzzled)…Joe Biden?\n\nThe model’s reasoning:\n>The answer indicates uncertainty and confusion about the question, stating ‘I’m afraid I’m not quite sure what you mean by the 46th US president.’ Although it eventually mentions ‘Joe Biden,’ the character expresses a lack of confidence and concern for other matters, which aligns with the criteria for ABSTAIN and ANSWER. The character does not provide a clear or confident answer without indicating a source or help.\n\nWe believe this interpretation is reasonable and consistent with our criteria, although in practice both humans labeled this as “NO ABSTAIN and ANSWER” because we think the characters gave the name with their own knowledge. Please refer to Section D in Supplementary Materials for more detail.\n\n### **Clarifying Figure 3 caption**\n\nWe apologize for the confusion in the caption. In Figure 3, we analyze model behavior for questions asked 30 years before and after each character’s death year. The x-axis shows the time distance from the death year: negative values (e.g., –30) indicate years before death, and positive values (e.g., +30) indicate years after. For instance, –30 corresponds to the question “Who was the U.S. president in <death year – 30>?” Each triangle represents the average response across all characters at that distance, with triangles specifically denoting data from Claude.\n\n\n### **More details in model’s hyperparameters**\n\nIn our paper, we ran the experiment with `do_sample = False` for Llama and Gemma. For Claude, we set the temperature to 0. For GPT-4o, we follow the common practice of setting the temperature to 1e-22 instead of exactly 0 to ensure good generation quality. We did not perform multiple rollouts per model, as all generations were deterministic. We will add the information in our final version. Our newly added experiments on CommonsenseQA above and our reproduction of our experiments with the U.S. president with `temperature=0.6` below show that **this choice is not critical for our experiments**.\n\nResults of standard prompt with `temperature=0.6`, averaged over 3 rollouts:\n|                                  |  Llama  |  Gemma  | Claude |  GPT   | Expected |\n|:--------------------------------|:-------:|:-------:|:------:|:------:|:--------:|\n| Abstention (Before) (%)         |  4.9    |  0.5    |  1.7   |  2.8   |   0.0    |\n| Abstention (After) (%)          | 18.2    |  2.3    | 13.6   |  0.3   | 100.0    |\n| Answer (Before) (%)             | 99.2    | 99.4    | 98.7   | 97.2   | 100.0    |\n| Answer (After) (%)              | 93.1    | 97.7    | 89.7   | 99.7   |   0.0    |\n| Conditional Accuracy (%)        | 90.3    | 91.2    | 99.6   | 91.7   | 100.0    |\n\n\n\nResults of standard prompt (in the paper):\n|                                  | Llama | Gemma | Claude | GPT  | Expected |\n|:----------------------------------|:-------:|:--------:|:--------:|:------:|:----------:|\n| Abstention (Before) (%)         |  4.6  |  0.0   |  0.7   | 2.6  | 0.0      |\n| Abstention (After) (%)          | 18.7  |  1.9   |  9.6   | 0.4  | 100.0      |\n| Answer (Before) (%)             | 99.3  | 100    | 99.3   | 97.5 | 100.0      |\n| Answer (After) (%)              | 93.8  | 98.1   | 92.6   | 99.6 | 0.0      |\n| Conditional Accuracy (%)        | 92.0  | 92.0   | 99.6   | 91.8 | 100.0      |\n\n\n[1] Talmor, Alon, et al. \"Commonsenseqa: A question answering challenge targeting commonsense knowledge.\" arXiv preprint arXiv:1811.00937 (2018)."}]

Author rebuttal:

Meta-review: This paper introduces and formalizes "concept incongruence" in LLMs and analyzes how conflicting concepts in prompts can cause unexpected behavior by focusing on a temporal role-playing scenario. The reviews were largely positive, with a majority of reviewers recommending acceptance. The primary initial concern, shared by several reviewers (u4PE, k8Bq, ZUxs), was the narrow experimental scope. The authors' rebuttal additional experiments on the CommonsenseQA dataset addressed this concern by demonstrating broader implications and justified the focused setting as a carefully-scoped foundational study. This convinced reviewers ZUxs and HBwf to raise their scores. Although one reviewer (k8Bq) remained unconvinced after a lengthy discussion, the overall consensus is that the paper introduces a novel and important framework. I agree with the reviewers and believe that this paper would be a valuable contribution to NeurIPS. I recommend acceptance.
Decision: Accept (poster)