Tracing the Traces: Latent Temporal Signals for Efficient and Accurate Reasoning
Authors: Martina G. Vilas
Safoora Yousefi
Besmira Nushi
Eric Horvitz
Vidhisha Balachandran
Venue: ICLR 2026 Poster (2026)
Keywords: Test Time Scaling
Reasoning
Interpretability
Representational Analysis
Primary area: interpretability and explainable AI

Abstract: Reasoning models improve their problem-solving ability through inference-time scaling, allocating more compute via longer and multiple token budgets. 
Identifying which reasoning traces are likely to succeed remains a key opportunity: reliably predicting productive thinking paths can substantially reduce wasted computation and improve overall efficiency. 
We introduce _Latent-Trajectory_ signals that characterize the temporal evolution of a model's internal representations during the generation of intermediate reasoning tokens. 
By analyzing both the extent and temporal course of latent representational change, as well as its alignment with the final state, we show that these signals are strong predictors of solution accuracy, outperforming conventional output-based confidence measures.
We use latent-trajectory signals to guide answer selection across multiple sampled generations, demonstrating that they make test-time scaling more effective and efficient, reducing token usage by up to 70% while preserving and even improving accuracy on average in comparison with majority voting. 
Finally, we show that these signals often emerge early in the reasoning trace, which enables early selection and allocation of compute to the most promising answer candidates during generation.
Our findings contribute not only practical strategies for inference-time efficiency, but also a deeper interpretability perspective on how reasoning processes are represented and differentiated in latent space.

Published as a conference paper at ICLR 2026
TRACING THE TRACES: LATENT TEMPORAL SIGNALS FOR EFFICIENT AND ACCURATE REASONING
Martina G. Vilas!2* Safoora Yousefi²
Besmira Nushi
Eric Horvitz?
Vidhisha Balachandran
'Goehhe University Frankfurt
Microsoft Research
NVIDIA
ABSTRACT
Reasoning models improve their problem-solving ability through inference-time scaling, allocating more compute via longer and multiple token budgets. Iden- tifying which reasoning traces are likely to succeed remains a key opportunity: reliably predicting productive thinking paths can substantially reduce wasted com- putation and improve overall efficiency. We introduce Latent- Latent-Trajectin Trajectory signals that characterize the temporal evolution of a model's internal representations dur- ing the generation of intermediate reasoning tokens. By analyzing both the extent and temporal course of latent representational change, well as its alignment with the final state, we show that these signals are strong predictors of solution accuracy, outperforming conventional output-based confidence measures. We use latent-traecctory signals to guide answer selection across multiple sampled gener- ations, demonstrating that they make test-time scaling more effective and efficient, reducing token usage by up to 70% while preserving and even improving accuracy on average in comparison with majority voting. Finally, we show that these sig- nals often emerge early in the reasoning trace, which enables early selection and allocation of compute to the most promising answer candidates during generation. Our findings contribute not only practical strategies for inference-tine efficiency, but also deeper interpretability perspective on how reasoning processes are rep- resented and differentiated in latent space.
INTRODUCTION
Recent advances in large language models (LLMs) have shown that complex reasoning tasks can be solved more effectively by scaling computing at inference time to generate longer and multiple chains-of-thoure (reasoning truces) and aggregating them into a final solution (Guo et al. [025]
Abdin et al. 2025 OpenAl 2024 Yang et al. (025a). However, not all reasoning traces are equal: while some contain productive steps that lead to correct answers, others may deviate into unproduc- tive behaviors such as overthinking, failing to converge
valid solution strategy, or exhibiting
inconsistent reasoning (Shojaee et al. 2025 Chen et al., 2024 Sun et al. 2025). Identifying the quality of reasoning trace (the likelihood of it leading
correct solution) is eritical. It not only enables more reliable prediction of correct answers, but it can also improve computational efficiency by potentially avoiding wasted effort on unproductive paths, and can provide feedback signals that enhance model training. By understanding which reasoning processes are effective, we can system- atically guide models toward reinforcing productive strategies and suppressing ineffective ones.
Prior work has approached this problem by inspecting reasoning traces in their surface natural- language form and identifying behaviors that lead to accurate answers, an approach that typically relies on costly human or model annotation strategies (Lee et al. 2025 Gandhi et al. 2023). In addition, natural language traces may not always reflect the underlying strategies that models employ (Chen et al. 2025 Stechly et al. 2025). and some models are trained to produce intermediate latent
Work done during internship at Microsoft Research.
Correspondence to maatinagvilas@cmm.mn
frankfurt.de and vidhishab microsoft.com
I Work done Microsoft Research

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Published as a conference paper at ICLR 2026
2024). Thus, language alone may be an unreliable proxy for evaluating reasoning trace quality. Other work has explored heuristic signals like trace length (Hassid et al. 2025; Marjanović et al. 2025), output distribution statistics (Kadavath et al.
embeddings rather than explicit text (Hao al.
2022 Yona et al., 2022). agreement-based self-consistency (Wang et al. 2023). or using trained
verifters (Li et al.
2023 Zhang et al., 2024) identify correct solutions, but these methods often
trade accuracy for simplicity, or computational cost for accuracy.
In this work, explore an alternative direction that solely leverages model's trajectory of hidden states to predict which traces lead to a correct solution. Previous studies have shown that probing hid- den states can reveal informative signals about safety (Turner al., 2023 Zou et al. 2023), learning dynamics (Olsson et al.) [022] Hosseini & Fedorenko, [202)), reliability (Meng et al. [022]] Yuk- sekgonul et al. 2024), and performance (Wang et al. 2024) of L.MMs. Building on this perspective, we hypothesize that the temporal evolution of hidden states during the generation of intermediate reasoning tokens contains predictive information about the final solution correctness, and can be leveraged for more compute-efficient and accurate inference.
We introduce a family of Latent- Trajectory (LT) signals that capture three complementary temporal aspects of model's internal representational trajectory (see Figure [ (i) the total
LATENT-TRAICTOON SCGNALS:
(4) not Change
(8) Canulative Change
(C) Aligned Change
Early Answer Selection
Trejechory
51
representational change from the start the end of the trace, (ii) the cumulative change taken along the way, and (iii) the extent to which the intermediate changes progress towards or away from the final state. These metrics operate directly on hidden states, require no additional training or external annotations, and can be computed during inference.
52
Layar
Early Trace Pruning
X
R
R
[R]
53
Our experiments evaluate the use of LT signals across families of reasoning- enabled LLMs (DeepSeek-R1-Distte Qwen14B, Reii-Reasoning-Pee Plus, Qwen3- 14B) and domains spanning science, math, and path optimization problems. We show that LT reliably distinguish
Figure 1: Latent-trajectory framework. Trajectory vec- are constructed from token-level hidden states, and a set of three signals is derived to quantify their tempo- ral evolution. These signals predict successful traces and enable answer selection and early path selection in multi-sample inference.
between traces leading to correct versus incorrect answers, yielding significantly higher discrim- inatory power than methods using other model internal or output-distribution---ars signals. In addition, we demonstrate that LT can be leveraged during inference to achieve both higher efficiency and improved accuracy. In sample scaling experiments, early answer selection using LT yields up to a 70% reduction in token usage, along with 2.5% average accuracy gain over majority-vote baselines by reducing the number of generations sampled. Finally, we show that these signals manifest in the early stages of the reasoning trace, allowing for early identification of promising candidates and targeted compute allocation.
Overall, our results show that a model's internal dynamics can be reliable predictors of reasoning quality, offering both practical tools for inference-time control and interpretability insights into how reasoning trajectories evolve, opening paths for broader applications that exploit internal signals for efficiency, accuracy, and calibrated decision- making..
2 RELATED WORK
Assessing Reasoning Quality: A growing body of work seeks to quantify the quality of reasoning traces in order to predict solution accuracy with high reliability, Many strategies involve employing verifier models, external or self, to assess the correctness of candidate answers (Weng et al. 2023 Madaan et al. 2023] Zhang et al. 2024). These approaches are effective but substantially increase inference cost. An alternative direction performs fine-grat -eaingr analyses of the trace surface form, proposing metrics that target factual and logical validity, as well as linguistic and semantic coherence (Wu et al. 2025 Golovneva al. 2022). Heuristics derived from output token distributions or from
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trace length have also been explored decide whether a path is likely to be accurate (Hassid al. 2025 Kadavath et al. 2022 Yona et al. 2022). Such methods often require annotation or structured extraction from traces, which introduces dependence on human raters or auxiliary expert models and can lead to model-specific heuristics. In contrast, LT signals are computed directly at inference time without teacher model additional runs, which yields more efficient procedure. Concurrent work trains model-specific probes over hidden representations to detect when intermediate answers are likely correct (Zhang al., 2025). Our approach shares the objective but remains training-free and can be applied to diverse models and datasets with minimal setup.
Representational Analysis: Previous studies have shown that probing an LLM's hidden states reveals informative signals about reliability (e.g. Meng et al. [222 Yuksekgonul et al.
2024). safety
(e.g. Turnner et al.) 2023 Zou et al. 2023), performance (e.g. Herrmann et al., 2025
Wang et al.
2024]. and learning dynamics (e.g. Olsson et al., 2022 Hosseini & Fedorenko, 2023) We extend
this research direction to leverage hidden states to predict accuracy reasoning models.
Recent efforts have demonstrated that information-theoredt rics capture intermediate representations that are more predictive of downstream performance than later layers (Skean et al. 2025). Closest to our approach, |Wang et al. (224)) examines representa- tional curvature across layers (i.e. spatial perspective) for predicting answer accuracy in instruction- tuned models. We differ by adopting temporal perspective across tokens and focusing specifically on reasoning models. Concurrent work (Li et al. 2025) extends sequential representational analysis to detect repetition loops in mathematical reasoning. further supporting the premise that temporal latent dynamics provide valuable insight into model behavior.
geometric, and invariance-based met-
Efficient Inference Scaling:
Scaling up inference-time computation is key factor in improving
reasoning performance in LLMs (OpenAl 2024] Guo et al. 2025 Abdin et al. 2025
While ef-
fective, previous studies (Balachandran et al., 2025 Shojace et al.) 2025 Sui et al. 2025 show that models trained to generate long reasoning traces exhibit 'overthinking' and consume compute even after reaching a correct solution. This has motivated efforts curb such behavior, either by train- ing models to produce more concise reasoning (Kang et al. [025a: Shrivastava et al.) 2025) or by dynamically halting trace generation once the model confident in its answer (Yang al. [0256) Zhang et al.) 2025). Another inference-time scaling strategy is to generate multiple samples and aggregate answers using self-consistency (Wang et al. 2023), external verifiers (Zhang et al. 2024),
or iterative self- sef--veifiiation (Madaan et al.)) 2023 Balachandran et al.) 2025). These methods boost accuracy for both standard and reasoning models but come with substantially higher computational cost. Recent work has sought to improve efficiency by pruning reasoning paths with trained classi- fiers (Manvi et al., 2024. Li et al. 2024). In contrast, our experiments show that Latent- Latent-Trajectory Trajectory Signals provide a training- way to guide reasoning path selection and answer aggregation.
3 LATENI-TRAJECTOR SIGNALS OF REASONING QUALITY
Given problem, reasoning models generate a sequence of tokens composed of reasoning trace followed by final answer. The trace is often delimited by special tokens ((troce-start), (trace_end)), such that:
9 ....... (trace.start) ff (t....,tttraaeeeen a1.....ay.
where ........ q. are the user query (problem) tokens, fi.....ty tr are the reasoning trace tokens, and a1..... ......... aj are the final answer tokens. For each position E {1...., , R} within the reasoning trace, the model produces a hidden state of activations at each layer E (1,.... I.J, denoted by 11")) e Rdd These hidden states form a 2D array of d-sized representations, indexed by layer and token position, and encode the latent space of the model at each step of the reasoning trace (see Figure I}.
3.1 LATENT-TRAJECTOR SIGNALS
We seek to characterize the quality of a model's intermediate reasoning by tracking the evolution of its internal representations through the trace. To quantify this, we average token-level activations into a sequence of segment-level states and extract trajectory signals that quantify the magnitude and geometry of representational change.
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First, to enhance the robustness of the signal and reduce dimensionality, divide the reason- ing trace fi....,tp into non-overlapiing reasoning segments, where each segment is a contigu- ous block of & tokens (k 5 For each transformer layer E {1....,L] and segment index n E [1, we compute the segment-level hidden state A/(") by averaging the token hidden states within that segment. Intuitively, h,()) corresponds the average representation the model maintains in latent space while processing segment n. This temporal coarse- graiinng smooths local fluctuations token-level dynamies while preserving the large-scale evolution of the model's latent space over the trace. The sequence (ht"),.. ..... h (N)) of the hidden-state evolution over the intermediate reasoning tokens.
at layer provides trajectory-level encoding
Given the segment hidden states, we define two basic vec- tors that anchor our trajectory signals. The reasoning drift vector: i = R b,() T,(1) captures the overall direction and distance the model's internal state travels during the trace, Complementarily, the update vector for segment n: "4 (m) T/(m) N, 7,1--1)
Cumulative Change
A
A/M
Σ 11
Latert Space
A
Alligned Change
...... N describes the incremental change between consecutive reasoning seg-
ments. Taken together, u, and of")) capture not only the overall extent of representational movement, but also the step-by-step dynamics of how that movement unfolds.
Net Change
Trace
Figure 2: Latent-trajectory signals.
From these primitives we derive three complementary signals that summarize (i) overall representational change, (ii) accumulated change the trace, and (iii) extent of progress towards the final state (see Figure Each signal is computed per layer and then averaged across layers to yield single score per trace.
Net Change. First, we explore whether intermediate reasoning substantially alters the model's latent space and whether such change is predictive of accuracy. To assess this, we measure the mag- nitude of representational change in the latent space between the first and last reasoning segment.
Formally, we measure the norm of the drift vector i at each layer, which encodes the magnitude of this change, and normalize by the number of segments control for trace length. Finally, average across layers to obtain a single score:
Iulk N
NETCHANGE = - Σ
IEL
Larger values indicate that the final hidden state has substantially changed from the initial state, suggesting that the reasoning steps produced significant overall change in representational space.
Cumulative Change. While Net Change measures the overall representational change between the initial and final reasoning segments, it does not characterize the intermediate latent-space changes. To summarize the total amount of representational movement along the trace, we addi- tionally compute the cumulative magnitude of the sequential updates to the reasoning trace.
We consider the update vectors iy (n) which represent the changes layer / between consecutive la gives the magnitude of change each step, and aggregating the norms across all segments captures the total movement along the trajectory. Finally, averaging across layers yields single score:
reasoning segments. The norm of the update vectors ||o
N
CUMULATIVECHANGE
1
nn) l12
IEL
Intuitively, Cumulative Change quantifies the overall shifts in representations during the course of reasoning, independent of the final states. Larger values encode significant variations in representa- tions across segments.
We experimented with various segmentation methods, including delimiters, See Appendix
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Aligned Change. Beyond measuring the magnitudes of overall and intermediate changes, ask whether intermediate updates tend to proceed in the same direction as the final outcome. We hypoth- esize that for reasoning traces leading to accurate solutions, the sequence of updates should mostly advance toward the final representation.
Formally, this is assessed by comparing each update vector of with the drift vector The cosine (m("),an) ..4)
similarity
||m!-)) la la||willa measures the angle between the two, indicating whether each local update proceeds in the same general direction as the overall displacement. Averaging across segments and
layers yiclds single score:
Σ LEL
N
(n) |
un
ALIGNEDCHANGE
= L N
n=
Higher values suggest that intermediate updates are aligned with the overall progress toward the final state, while lower values indicate they are inconsistent or even opposed to it.
4 EXPERIMENTAL SETUP
4.1 BASELINES
We alternative approaches baselines: (1) Cross-Layer Signals, which summarize repre- sentational changes across layers within a segment, and (2) Output Distribution Measures, which estimate confidence from the token distribution at the final answer.
Cross-Layer Signals: Previous work has shown that changes across layers can be predictive of answer accuracy in COT. Following (Wang et al., 2024), for each reasoning segment n, we com- pute the mean magnitude and angle of layer-to-layee changes and then average over segments:
LAYERMAG (n)
1 L
Tu hn 112e,
LAYERANG (m)
arccos(cos(hiy (u, hy (rocos(cos(h,,, 1u))
1 h1ll2
Output Distribution Measures: Output distribution based measures are commonly used as esti- mates model confidence (Yona et al. 2022] Kadavath al., 2022 Manakul et al., 2023). and can be used as proxies for final answer reliability. To compare against these metrics, we elicit the final answer post the reasoning trace end using prompts of the form [... (trace_end) Final Answer 1. and examine the probability distribution over the token that follows. Based findings from Yona et al. (2022), we considered three best performing output distribution measures: (i) Logit Margin: the difference between the top-two token logits; (ii) Entropy: the entropy of the token distribution. (iii) Perplexity: computed as the inverse probability the model's top-ranked token, providing scalar measure of confidence in its most likely continuation.
4.2 MODELS AND DATASETS
For our main experiments, we evaluate three open-source reasoning models: DeepSeek- R. -Distill-- Qwen- 14B (R1-D) (Guo al. 22025). Phi-4 Reasoning Plus Model (PH14R+) (Abdin et al.) 2025). and Qwen3-14B (QWEN3) (Yang et al. (025a). Our study tests our LT signals across three distinct reasoning domains: (i) Scientific, measured using the GPQA Diamond benchmark, which comprises 198 graduate-level multiple-choice questions in biology, chemistry, and physics (Rein et al. 2024);
(ii) Mathematical, evaluated on AIME 2025, a a0.5,a300p-bbbbb. 20-problem set from the American Invitational Mathe- matics Examination (AIME, 2025); (iii) Algorithmic. assessed with stratified subsample (n 180)
of the TSP benchmark, consisting of path-optimization problems across varying levels difficulty (graphs of 6 to 13 nodes) (TSP 2025
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Metric
Net Charge
Cumulattie Change
Aligned Change
Layer Mag
Layer Ang
Logt Margih
Entrosy
Perplexity
1.0
GPQA
1.0
AIME2025
1.0
TSP
0.
0.0
0.9
0.8
3.8
0.8
0.7
205
Figure 4: ROC-AUC for distinguishing correct from incorrect predictions using Latent- Trajectory (LT) and baseline metrics. Higher values indicate better discriminative power. For comparability, Cumulative Change and Layer Angle metrics were sign-reversed. LT signals consistently achieve above chance (dashed line) and more reliable discrimination than baseline metrics. Error bars denote variability across models.
5 RESULTS
5.1
LATENT-TRAUECTORY SIGNALS ARE PREDICTIVE OF SOLUTION ACCURACY
To assess whether LT signals predict solution correctness, we evaluate their discriminative power us- ing the area under the ROC curve (AUC). For each problem, we generate five independent solutions (reasoning trace and final answer). For each trace, we compute the LT scores from the hidden states of the intermediate reasoning tokens. We then compute the ROC-AUC of the signal with respect to accuracy by sweeping a decision threshold over the scores, which captures how well the signals discriminate solution correctness.
We found that Cumulative Change was negatively corre- lated with accuracy (Spearman's ss -38), which indicates that traces that traverse greater total distance in representation space tend to be less likely to produce correct answers. This finding mechanistically grounds prior behavioral observations that long but highly varying reason- ing traces are associated with lower accuracy (Balachandran 2025; Shooaaee et al. 2025). In contrast, Net and Aligned Change show positive associations with accuracy (Net Change r = .28; Aligned Change -32). Larger overall representational change from the initial the final hidden state is therefore linked to better performance, and representational updates that progress more directly toward the final state show an even stronger association. Figure 3 shows the distributions of the three trajectory metrics for Qwen3 on AIME2025. The distribution of values further supports our findings: successful trajectories cover greater distances in latent space, advance more directly toward the final state at intermediate steps, and involve less path deviations. Equivalent plots for each model and dataset are in Appendix [ including plots of layer-wise values for each LT signal.
Owen3 148 AIME2025
Net Change (1)
Cumulative Change (4)
Aligned Change (1)
360
6220
3504
201
5330
16K
5.0%%
Accuracy
Accuracy
Accuracy
Figure 3: Latent-trajectory signal distributions by accuracy for Qwen3-14B on the AIME 2025 dataset. Correct traces show larger Net/Aligned Change and smaller Cumulative Change than incorrect ones. This indicates that correct reasoning corresponds to larger, more directed representational shifts, while incorrect reasoning involves more wandering and less aligned trajectories.
et al.
As shown in Figure LT signals significantly distinguish between reasoning traces that lead to ac- curate versus inaccurate solutions. Across datasets, the ROC-AUCs of our three LT signals remain consistently above chance, demonstrating robust predictive power (Net Change mean ROC-AUC signed-reversed Cumulative Change = 0.74 0.09; Aligned Change = 0.73 0. 0.08). In contrast, the cross-layer magnitude and angle signals are less reliable and vary substantially across models and reasoning domains (Cross-Layer Magmitude Change = 0.58 + 0.17; reversee Cross-Layer Angle Change 0.67 + 0.14). Dutput-distributionn based metrics significantly weaker and less consistent, with performance often close to or below chance level (Logit Margin 0.59 + 0.10; Entropy 0.44 + 0010, Perplexity 0.49 + 0.12). In summary, our results show

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that for models that produce long intermediate traces, signals that capture the temporal evolution in latent space are stronger and more robust predictors of solution accuracy than cross-layer geom- etry or sitput-distributiin correlation values for each model-dataset combination).
confidence measures (see Appendix B for ROC-AUC scores and
To more comprehensively assess the predietivity and robustness of the LT metrics, Appendix B broadens the evaluation to include additional models of distinct sizes and architectures (DeepSeek- RI-Llama 8B; DeepSeek-RI-Qwee 32B; DeepSeek-RI-Llama 70B) and a suite of reasoning prob- lems that entail divergent reasoning, including commonsense, creative, narrative, and socially grounded tasks (Srivastava al. 2023). We observe that our LT signals remain significantly pre- dictive of answer correctness across the new models and reasoning domains. The ROC-AUC values for Net, Cumulative, and Aligned Change remain well above chance and are comparable to those reported for the main benchmarks (Net Change mean ROC-AUC = 0.71 + 0.09; Cumulative Change 0.72 0.09; Aligned Change = 0.70 + 0.10).
5.2 TRAECTRRR SIGNALS IMPROVE RFFICIENCY AND RELJABILITY OF MULTI-SAMPLE INFERENCE
Building on our previous finding that LT signals strongly predict solution accuracy, we now inves- tigate whether they can guide more accurate and efficient scaling strategies for sampling, selecting, and aggregating solutions multi-sample inference systems. Previous work demonstrates that gen- erating multiple samples and aggregating them through self-consistency improves both accuracy and reliability in language models (Wang et al. 2023 Kang et al.) (2025b). In practice, majority voting (MV) has become the default approach for recent releases of reasoning models (Abdin et al. 2025 Guo et al. 2025), since a single inference pass is rarely sufficient for robust performance, especially in applications or agentic settings (Besta et al. 2025). This robustness, however, comes with in- creased inference costs, particularly for reasoning models, where long chains of thought lead token usage to grow by an order of magnitude with each additional sample. Here, we examine whether L.T-based selection can preserve the benefits of MV while reducing sample and token budget.
We generate multiple samples sequentially from the model and use LT signals to decide online whether the current trace is likely correct and should be used as the final answer, or whether additional samples are needed. Once signal exceeds a calibrated threshold, we accept the solution early and stop sampling. If no samples cross the threshold after at most l attempts, we fall back to MV over the collected candidates (see Figure 3). This allows datapoints with strong internal signals to be resolved quickly with fewer samples, while datapoints with weaker signals rely on the robustness of aggregation. We set
Experiment Setup:
and repeat this procedure independently for each signal.
We compared our approach against two sample aggregation baselines: (i) MV, and (ii) shortest- answer selection, which chooses out of the sampled answers the candidate with the fewest tokens, motivated by recent findings that shorter completions strong signals of accuracy (Hassid et al.
2025 Shrivastava et al., 2025 Marjanović et al. 2025).
We select decision thresholds using a cross-validation approach (see Appendix forr details). On the calibration set, we form candidate thresholds from quantiles of the metric among incorrect solutions, so each candidate fixes the proportion of incorrect solutions that lie beyond the cut- off. For each candidate, we simulated the full decision rule on the calibration sub- set, accepting a solution carly when the signal crosses the threshold and otherwise aggregating with MV. The best-calibrated threshold is then evaluated on the remaining data. Appendix G further explores a global threshold selection strategy that obtains similar results dataset-specifie approaches.
Datapoint
Datapoint 2
Early
Majority
53
Stopping
51
Vote
Figure 5: Candidate solutions for a problem are evalu- ated sequentially. If solution's 's signal value exceeds T, it is immediately accepted as the final prediction. no solution crosses 7, the final answer is chosen via MV.
We report accuracy as the fraction of problems solved correctly, and efficiency in terms of (i) the average number of samples required, and (ii) the proportion of reasoning tokens consumed relative
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to running the full inference procedure with five samples. Reported results are averaged over the splits.
In addition to exploring cach metric separately, we built Combined LT score from weighted sum of the LT values, where signals that are more strongly associated with accuracy on the calibration set contributed more to the final score (Appendix repoorts details on its construction).
Results: As shown in Table I LT signals improve both efficiency and accuracy relative to self- consistency based majority vote. On GPQA, R1-D gains about 2%, Qwen3 remains stable, and Phi4R+ maintains competitive accuracy. On AIME2025, improvements are more pronounced with 4% for RI-D, 2% for Phi4R+, and a substantial 12% for Qwen3. On TSP, all models benefit, with consistent gains of 1-3%. These results show that LT thresholds not only preserve correctness across settings, but often deliver meaningful boosts by identifying correct reasoning paths even when the majority of solutions are incorrect. Efficiency gains are considerably larger. On R1-D, LT signals reduce the average number of tokens required to match or outperform MV by 50-66% across datasets, Qwen3 achieves reductions of about 50-55%, and Phi4R+ reduces sampling by 30-35%. Across all settings, the Shortest@5 5 baseline reduces accuracy by an average of 1.4%, showing that length alone is unreliable proxy for correctness. In Appendix I we further report that LT signals trigger early answer selection for >85% of data points on average across datasets, and that accuracy within this subset consistently exceeds the baselines, confirming that LT signals concentrate probability mass on more reliable solutions.
Overall, Latent-Trajeccie Traterroorregggddeee selection preserves, and often improves, the reliability of majority-vose aggregation while substantially reducing inference costs, offering a reliable alterna- tive fixed-sample aggregation. The Combined LT score is frequently competitive with the best individual signal and, in most cases, cuts token usage by at least half. This makes it a practical and effective choice when applied to different models and datasets. At an aggregate level, compared to MV@5, LT strategies exhibit: (i) Sample savings: Number of sampled answers is reduced on av- erage by 58% (32.0-5..6% ) (ii) Token savings: As consequence of sample savings, token usage (and thereby inference cost) is reduced on average by 48% (14.5-70.6%) (iii) Accuracy improve- ment: Accuracy increases on average by 2.46 % (-1.4-14.1%)
Table 1: Accuracy and efficiency with Latent- Trajectorr (LT) signals. Basclines MV@5 (ma- jority vote across 5 samples) and Shortest@5 (shortest of samples). Accuracy is reported as a percentage, with parentheses indicating the change relative to MV @5. For efficiency, we report the average number of samples required per datapoint, with parentheses showing the percentage re- duction in total token usage relative to MV. Bold and Bold denote the best and second-best results within each group. marks cases where the average number of samples was reduced more than half.
GPQA
A1ME2025
TSP
Model
Strategy
Acc.
Samples (avg/ ATok %))
Acc.
Samples (avg/ ATok %)
Acc. (avg S AAcc)
Samples (avg / Tok 55)
(avg / AAcc)
(avg % AAcc)
MS
59.90
5.00
56.67
5.00
27.50
5.00
Shortect@5 5
60.91 (+1.0)
5.00 (0)
50.00 (-6.7)
5.00 (0)
28.75 (+1.3)
5,00 (0)
LT Net
61.10 (+1.2)
1.69 (+53.9)
61.90 (+5.2)
1.22 (+68.7)
28.60 (+1.1)
1.43 (+70.6)
RI-D
LT
Comulative
62.10 (+2.2)
1.88 (+48.1)
58.70 (+2.0)
2.56 (+29.9)
30,90 (+3.4)
1.61 (+66.4) v
LT Aliguod
61.10 (+1.2)
1,58 (+57.0)
60.30 (43.6)
1.43 (+61
29.50 (+2.0)
2:08 (+57.2) v
Combined
61.80 (+1.9)
1.89 (447.3)
61.90 (+5.2)
(+43.9)
30.10 (42.6)
(+70.3) /
MV@s
70.20
5.00
80.00
5.00
41.25
5.00
Shortest@ 5
69.19 (-1.0)
5.00 (0)
70.00 (-10.0)
5.00 (0)
38.75 (-2.5)
5,00 (0)
Phi4R+
LT Net
68.80 (-1.4)
2.97 (+20.2)
79.40 (-0.6)
2.19 (+1111))
42.30 (+1.1)
1.59 (+67.2)
Cumulative
(-0.6)
(+18.9)
(+1.0)
(+32.11
44.40 (+3.1)
(+42.22
LT Aligned
69.60 (-0.6)
3.40 (+14,5)
82,50 (+2.5)
2.51 (+30.8)
44.10 (+2.9)
1.66 (+58.7)
Combined
3.28 (+16.3)
82.60
(+28.7)
43,80 (+2.6)
(+50.5) v
MV
63.96
5.00
70.00
5.00
36.25
5:00
S
64.47 (+0.5)
5.00 (0)
80.00 (+10.0)
5.00 (0)
30.63 (-5.6)
(0)
Qwen3
LT Net
63.70 (-0.3)
1.42 (+63.9)
79.40 (+9.4)
1660 (+57.3)
35.40 (:0.9)
3.18 (+34.0)
Cumulative
(-4.7)
2.25 (+41.3)
84,10 (+14.1)
(+-13.2)
36.30 (+0.1)
(+65.5) v
LT Aligued
64.20 (+0.2)
1,75 (+52.0)
80.90 (+10.9)
1,59 (+58.2) <
37.80 (+1.6)
2.08 (+56.4) <
IT Combined
63.70 (-0.3)
1,70
(453.4)
80,90 (+10.)
1.49 (+60.3)
36.00 (-0.3)
2.46 (+48.4)
5.3 LATENT-TRAJECTOO SIGNALS ENABLE EARLY SELECTION OF HIGH-QUALITY TRACES
While the previous section focused on using LT signals for end-of-trace an- swer selection, now ask whether these signals also identify higher-quality trajectories early
Experiment Setup:
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Published as a conference paper at ICLR 2026
in the reasoning process. To investigate this, We evaluate signals on partial traces taken at 500-token intervals up to the full trace. At each checkpoint, we recompute Net Change and Cumulative Change usiig only the tokens available so far. For each partial trace, the most recent segment is used as the final segment. We then compute the ROC-AUC of each signal every checkpoint, revealing how predictive power evolves as the trace unfolds, and whether prediction of solution correctness is possible without observing the full trajectory.
step-wise incremental early-exit evaluation.
Chaing
To investigate whether these early signals in the reasoning trace can be leveraged during inference, we implement an early path selection policy when sampling multiple generations parallel. We compute the LT signals on the partial traces and use them as features for a lightweight random forest classifier trained to predict correctness. The classifier selects a single candidate trajectory to continue, while the other four paths are terminated. The chosen trace is decoded to completion, and we report both the accuracy of this early path selection policy and the proportion of tokens saved compared to running all five trajectories to the end. We also compare the performance of our method against that of Wang et al. (2025) (ST-BoN), which selects the sample to be completed until termination based on sample pairwise distances of cross-layer signals.
As Figure 6 shows, Net and Cumulative change provide predictive signals well above chance, with ROC-AUC generally increasing as additional tokens are observed. ROC-AUC values above can be obtained within the first 4k tokens, with Net Change being a better predictor than Cumulative Change early in the trace for GPQA and AIME2025. This pattern, however, reverses for TSP, where Cumulative Change is significantly more predictive than Net Change throughout the early and mid trace.
Results:
(A)
(B)
GPOA
AIME2025
TSP
Prediction at 4,000 tokens
90-
1.
60-
Metric
67-
6.7-
Net Charge
38
Fumulative Change
Layer Mag
35
35
Number of tokens
Number of takena
Number of tokens
Datuset
Figure 6: (A) Predictive performance (ROC-AUC) of Net and Cumulative Change signals as a func- tion of the number of tokens, across datasets. Shaded regions represent variation across models. (B) Comparison of predictive performance at 4k tokens. Error bands indicate variation across models. Performance of LT signals rises early well above the bascline.
Table 2 shows that early selection of high quality paths with LT at 2k tokens reduces inference cost while maintaining or improving accuracy. Across models and datasets, accuracy remains highly competitive with MV @ 5. R1-D achieves 6. 7% gain on AIME2025 with competitive performance on GPQA and TSP. PhiR+ consistently improves accuracy by 2-4% across datasets, while Qwen3 yields gains of 2-3%. Efficiency gains more significant, with average token usage reduced by 54% for R1 RI-D, 62% for Qwen3, and 68% for PhiR+. At an aggregate level, we observe: (i) accuracy increases average by 2.49% (ii) token usage reduction on average by 61.2%. Appendix Oppendix[further ffutterr demonstrates that these results are consistent across early point checkpoint selection. Overall, these findings demonstrate that LTT signals can effectively guide early selection of high-quality paths, allo- cating compute to the most promising generation candidate, thereby achieving competitive accuracy to five-sample majority vote with less than half of the inference cost.
In addition, we also found that across nearly all model-dataset settings, LT-based early selection matches or exceeds the accuracy of ST-BoN while consistently delivering comparable or greater token savings. LT's deviations are smaller in comparison and its improvements are more uniform, frequently achieving the highest accuracy. Across models and datasets, LT delivers more than dou- ble the average accuracy gain of ST-BoN (+2.48% vs +1. +1.12%) at the same rate of token savings.
2 As Aligned Change compares the direction of each segment with respect to the last segment, it is inconsis- tent when applied earlier in the trace.

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Published as a conference paper at ICLR 2026
Importantly, LT remains computationally cheaper, since it operates the latent trajectory of single sample rather than computing pairwise distances across multiple samples as required by ST UT-BoN.
Table 2: Evaluation of early path selection (at 2k tokens) using LT (LT) signals. Accuracy (%) and
Saved Tokens (%) with A relative to Majority Vote M).
GPQA
AIME2025
TSP
Accuracy A
Saved Tokens
Accuracy A %)
Saved Tokens
Accuracy / A %)
Saved Tokens (A %)
Model
Strategy
(A %)
(A
RI-D
Maj@5
59,90
56.67
27.50
ST-BoN
58.88 (-1.02)
+45.3
63.33 (+6.7)
+49.7
28.75 (+1.2)
+62.8
LTT
59.39 (-0.5)
+48.9
63.33 (+6.7)
+50.1
26.25 (-1.3)
+62.5
Phi4R+
Maj@5
70.20
80.00
41,25
ST-BoN
66.16 (-4.0)
+62.3
76.67 (-3.3)
+67.4
42,50 (+1:25)
+71.4
LT
72.22 (+2.0)
+64.7
83.33 (+3.3)
+67.3
45,63 (+4.4)
+71.7
Qwen3
Maj@s
63.96
70.00
36,25
ST-BoN
63.63 (+2.54)
+46.8
76.67 (+6.7)
+67.4
36,25 (+0.0)
+65.8
LT
66.50 (+2.5)
+51.0
73.33 (43.3)
+69.1
38.13 (+1.9)
+65.7
CONCLUSIONS
Our work introduced a family of LT signals that capture the temporal evolution of reasoning traces within a model's latent space. Across multiple reasoning domains and models, LT metrics pre- dict final-answer correctness significantly above chance and outperform other internal and output- distribution-bart baselines. We further demonstrated their utility in practical rest-time policies. In inference-scaling experiments, using these signals for answer selection or early path selection reduced token usage and often improved accuracy with respect to strong baselines such major- ity vote. Our efficiency gains address two complementary sources of inefficiency: (i) reducing the number of samples required for reliable reasoning, and (ii) shortening individual trajectories by de- tecting early answers of higher-quality. The approach model-agnostic, simple calibrate, and compatible with existing sampling strategies. In addition to practical benefits, results shed light on the structure of reasoning in latent space, revealing how trajectories unfold during inference and what distinguishes successful from unsuccessful reasoning paths.
There are several opportunities for future work. While we show the real-world utility of these signals inference time, trajectory-level signals could also provide actionable guidance for fine-tuning and calibration, with the potential to guide models toward more reliable reasoning trajectories. LT- derived metries could be incorporated training-time regularizers that encourage more aligned and productive trajectories. In addition, our study introduced lightweight techniques for metric aggregation and threshold selection. An exciting direction for future work is explore learned classifiers or ensembles to further boost the informativeness of the signals. Finally, LT signals are designed as predictive, representation-leve indicators of model performance, not as direct causal explanations. Uncovering the causal mechanisms underlying these trajectories remains an open and compelling question for future interpretability research.
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REPRODUCIBILITY STATEMENT
Section outlines the models, datasets, and methodology used in detail. Section K in Appendix describes inference parameters in detail. The data and code be found at http://gthhb..oommhaaiiivvvllggp
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A LATENT-TRAJECTON SIGNALS
To investigate how LT dynamics relate to model performance, we compare distributions of the three signals-NWe Net Change, Cumulative Change, and Aligned Change-conditions on whether a model's final answer was correct incorrect. Figures 7 78 and present box plots of these metrics across datasets and model families. These visualizations allow us to assess whether systematic differences LT signals are associated with answer correctness, and whether such effects are consistent across evaluation settings.
Across all three signals, consistent patterns emerge. Net Change values (Figure are generally higher for correct responses than for incorrect responses, suggesting that successful reasoning is sociated with overall larger representational drifts. In contrast, Cumulative Change values (Figure often larger for incorrect responses, indicating that when models answer incorrectly, their latent trajectories tend to involve more movement through representational space, potentially reflecting less stable reasoning. Finally, Aligned Change values (Figure 9 again higher for correct sponses, implying that effective reasoning requires updates that advance more directly towards the final state.
Taken together, these results suggest that correct predictions are characterized by a larger overall representational shift, accompanied by trajectories that are more directionally consistent, whereas incorrect predictions tend to involve longer, less aligned paths through latent space, reflecting noisier and less stable reasoning trajectories. This pattern holds across models and datasets, indicating that LT signals provide complementary and reliable signals of reasoning quality.
Distribution of Net Change by Accuracy
AIME2025
TSP
20.0
225
Phi4
20.0
1.5
2.6
0.0
0.5
125
R1-D
75
Qwen3
Correct
Incorrect
Correct
Incorrect
Correct
Incorrect
Figure 7: Distribution of Net Change by accuracy. Values are generally higher for correct than for incorrect responses, suggesting that successful reasoning is associated with overall larger represen- tational drifts, which may be a sign of deeper reasoning.
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Distribution of Cumulative Change by Accuracy
1293
1230
1993
1993
Phi4+
803
693
900
403
402
R1-D
2000
1000
Qwen3
693
1093
Incorrect
Incorrect
Correct
Figure 8: Distribution Cumulative Change by accuracy. Values are often larger for incorrect responses, indicating that when models answer incorrectly, their latent trajectories tend to involve more movement through representational space, potentially reflecting less stable reasoning.
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Distribution of Aligned Change by Accuracy
1.6
25
Phi4+
2.6
0.84
0.02
34
488
0.20
0.2
0.135
2.6
qwen3
0.5
0.350
0.20
0.125
0.000
0.005
Incorrect
Incorrect
Correct
Incorrect
Figure 9: Distribution of Aligned Change by accuracy. Values are higher for correct responses, implying that effective reasoning involves intermediate representational updates that advance more directly towards the final state.
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We also provide the average layer-wise values of each LT signal in Figure 10 subplot compares how internal changes evolve by layer.
and
12
Each
Layer-wise values of Net Change by Accuracy
GPQA
AIME2025
TSP
4.0
27.0
3.5
Phi4+
1.5
1.0
34
25
R1-D
30
15.0
30
Qwen3
10.0
5.0
25
Incorract
Correct
Incorrect
Correct
Incorract
Correct
Figure 10: Layer-wise Net Change values for correct vs. incorrect reasoning traces mod- els and benchmarks. Correct trajectories generally show larger representational shifts across layers compared incorrect ones, indicating that stronger changes are associated with successful reason- ing.
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Layer-wise values of Cumulative Change by Accuracy
Layer
GPQA
AIME2025
TSP
1993
2200
Phi4+
1393
10:0
730
1093
1780
1200
1300
2000
R1-D
1093
750
300
200
6000
1200
5033
Qwen3
2000
4300
3003
600
2003
1000
Incorrect
Correct
Incorrect
Incorrect
Correct
Figure 11: Laser-wise Cumulative Change values for correct vs. incorrect reasoning traces across models and benchmarks. Incorrect trajectories accumulate substantially larger representational shifts across layers than correct ones, indicating that correct traces take more direct paths towards the final solution.
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Layer-wise values of Aligned Change by Accuracy
Layer
GPQA
AIME2025
TSP
0.66
Phi4+
0.04
2.10
0.05
0.02
2.08
2.00
3.00
0.00
3.40
0.24
2.36
3.05
3.06
2.00
3.00
0.00
2.40
0.30
9.36
2.30
Owena
1.26
2.08
0.62
3.00
Correct
Incorrect
Incorrect
Figure 12: Liser-wise Aligned Change values for correct vs. incorrect reasoning traces across models and benchmarks. Correct trajectories consistently exhibit stronger alignment across layers compared to incorrect ones.
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B PREDICTIVITY OF LATENT- TATENNTTRAJECTORY SIGNALS
In Table we provide the ROC-AUC and correlation (Spearman's 's values with accuracy for each Latent- Frajettrr (LTT) and baseline metric computed per model-datase pair.
Table 3: ROC-AUC and correlation (Spearman's r) with accuracy for each model-dataset pair. Cu-
mulative Change and Layer Angle signed reversed for ROC-AUC computation.
GPQA
AIME2025
TSP
Model
Metric
AUC
Com
AUC
Corr.
AUC
Corr.
Net Change
.688
.320
.757
.433
.64
.223
Cumulative Change
.690
-333
.697
-3333
.687
-299
Aligned Change
.670
.288
.755
.430
.662
.255
Layer Magnitude
.606
.180
.795
.497
.449
-000
RI-D
Layer Angle
.392
.184
.685
-.313
.740
-3378
Logit Margin
.092
.597
.163
.656
.246
Entropy
.500
.016
.488
-020
.88
.138
Perplexity
.656
.266
.558
.097
.597
.153
Net Change
.744
.391
.755
.366
.625
.433
Cumulative Change
.740
-3844
.786
-.100
.785
-.333
Aligned Change
.744
.391
.755
.366
.733
.430
Layer Magnitude
.730
.368
.625
.180
.768
PhiR+
Layer Angle
.663
-,26
.727
-.325
.733
-3112
Logit Margin
.652
.243
.535
.050
.438
.163
Entropy
.230
-,321
.440
-08
.426
-000
Perplexity
.369
-.194
.517
.024
.399
.097
Net Change
.671
.286
.921
.651
.637
.229
Cumulative Change
.655
-,259
.947
-691
.748
-414
Aligned Change
.635
.225
.909
.632
.679
.300
Layer Magnitude
.557
.095
.295
-311
.387
-1199
Qwen3
Layer Angle
.509
-0015
.878
-.584
.727
.380
Logit Margin
.444
-0044
.728
.351
.602
.170
Entropy
.513
.022
.286
-.324
.371
Perplexity
.272
-380
.454
-071
.591
.151
Our main experiments focus domains where previous work has shown that reasoning models exhibit stable and well-understood behavior (i.c., scientific, mathematical, and algorithmic tasks), and thus where these models mainly applied to. To further assess whether latent-trajectory signals also generalize to more "divergent" forms of reasoning, we expanded our analysis to in- clude tasks that require more commonsense-based, creative, narrative, socially grounded forms of reasoning.
Specifically, we evaluated metrics isting reasoning models have not saturated performance and where the required cognitive processes differ substantively from the domains tested in the main paper: (i) Understanding Fables: Measures the ability of models to identify the most suitable moral for a given fable, targeting narrative abstrac- tion, commonsense, and creativity; (ii) Social IQa: Measures the ability of models to reason about the common-sente implications of social situations, targeting social reasoning, emotional inference, and context-sensitive commonsense. Because new tasks generate shorter reasoning paths, we report the results obtained when averaging reasoning segments of 100 tokens.
BIG-Bench tasks (Srivastava et al. 2023 where
In addition extending our analysis to new reasoning domains, we also verify that our metrics are predictive in open-source models of different sizes and architectural family. Concretely, we probe DeepSeck-RI- -I-Lammaa 8B, DeepSeek-BB -wenn 32B, and DeepSeck-RI RI-Llmma 70B.
As Table shows, observe that our LT signals remain significantly predictive of answer correct- ness across the new models and reasoning domains. The ROC-AUC values for Net, Cumulative, and Aligned Change remain well above chance and are comparable to those reported for the main bench- marks (Net Change mean ROC-AUC = 0.71 + 0.09: Cumulative Change = 0.72 0.09; Aligned Change 0.100. The cross-layer signals exhibit substantially higher variability across models
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Table 4: ROC-AUC scores of LTT metrics and Cross-Layer signals extended models of different sizes and architectures, and reasoning problems of different domains. Reported with segment sizes of 100 tokens.
Model
Change
GPQA
AIME25
TSP
BB Fables
BB Social
RI-Qwen- 14B
Net
.836
.636
.786
R R1-Qwen-16e 14B
Cumulative
.710
.732
.685
.660
.618
R1-Qwen- 14B
Aligned
.709
.720
.570
.674
.623
RI-Qwen- 14B
Layer Mag.
.605
.788
.693
.710
.556
RI-Qwen-
Layer Ang.
.388
.672
.387
.354
Phi4-R+-14B
Net
.708
.807
.708
.778
.713
Phi4-R+-14B
Cumulative
.802
Phi4-R+-14B
Aligned
.752
.797
.718
.777
.714
Phi4-R+-14B
Layer Mag.
.773
.773
.780
.773
.743
Phi4-K Phi4-R+114B
Layer Ang.
.008
.798
.14
.799
.780
Qwen3-14h
Net
.655
.977
.668
.839
.696
Qwe33- B4B
Cumulative
.687
947
.713
.844
.683
Owen3-14B
Aligned
.910
.531
845
Owen3-144
Layer Mag.
.787
.760
.559
Qwen3-14B
Layer Ang.
.806
.006
.707
.818
.656
RI-Llama- 8B
Net
.633
888
.144
.653
.595
R1-I.lama-8B
Cumulative
.628
.94
.582
.651
.587
R1-Ilama-8B
Aligned
.642
.900
.525
.657
.590
R R1-I_lma-88
Layer Mag.
.399
.784
.623
.618
.572
RI-Llama-88
Layer Ang.
.448
.865
.448
.471
.464
RI RI-Qwen-322
Net
.705
.055
.662
.756
.639
R Qwen-32nB
Cumulative
.691
814
.690
.732
.629
R R1-Q Qwen-32B
Aligned
.712
.821
.579
.746
.639
R R1-Qwen-32B
Layer Mag.
.622
.403
.667
.610
.590
R R--Qwen-32B
Layer Ang.
.671
.815
.606
.699
.635
R1-I.lma--705
Net
.694
.837
.696
.786
.658
R RI-Llama-77B
Cumulative
.697
.856
.707
.004
.652
R1-1.lama-700
Aligned
.857
.701
.805
RI-Llama-700
Layer Mag.
.417
.169
.620
.510
.516
R 21-LLlam--70B
Layer Ang.
.326
.705
.595
.492
.497
and datasets (Cross-Layer Magnitude Change mean ROC-AUC 0.56 + 0.18: Cross-Layer Angle Change 0.56 + 0.18).
These results suggest that the temporal structure capture in latent space reflects broader phenomenon in ceasoning-orientee model behavior, not one limited to particular architectures or problem-solving tasks. We believe these metrics remain predictive even in more "creative" tasks because they quantify relative differences between correct and incorrect trajectories rather than as- suming any particular structure of the task. Even domain naturally produces larger or more exploratory latent movements overall, correct trajectories still tend to show more consistent and di- rected progression toward their final internal state, whereas incorrect ones typically wander more exhibit less alignment. Thus, the absolute scale of representational change may vary across do- mains, but the contrast between successful and unsuccessful paths remains robust, which what the LT signals capture.
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C CALIBRATION PROCEDURE FOR THRESHOLD SELECTION
We use a three-fold shuffled cross-validation procedure. In each split, 30% of the data is set aside for calibration and the remaining 70% is reserved for testing, with the same random seed applied across folds to ensure consistency.
Candidate thresholds. Within each calibration fold and for each metric, we focus on the subset of datapoints where the model's answer was incorrect. If this subset contains fewer than 15 examples or the metric has no valid values, we default to using the median value of the metric on the calibration set the threshold. Otherwise, we construct grid of candidate thresholds by taking the 20th through 99th percentiles the metric values among the incorrect examples.
Evaluating candidate threshold. For each threshold in this grid, calibration datapoints are di- vided into two groups The first group consists of datapoints where at least one candidate solution exceeds the threshold. For these, accept the first candidate that crosses the threshold and record its accuracy. The second group contains the remaining datapoints, which are resolved using majority vote across their candidates. The overall calibration accuracy for given threshold is computed as the weighted average of the accuracies from these two groups, proportional to their sizes.
Selecting the threshold. We then rank thresholds by their overall calibration accuracy. The two best-performing thresholds are identified, and we set the final calibration threshold to their median.
Direction of comparison. For most metrics, higher values indicate stronger signals, so the thresh- old rule is applied as metric 2 The Cumulative Change signals behave the opposite direction, with smaller values being more predictive; here the rule is applied as metric S t.
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D COMBINED LATENI-TRAJECTOR SCORE
In addition to exploring each metric separately, we built a Combined LT score. For each dataset, quantified the predictive utility of each signal by calculating its absolute Pearson correlation with accuracy on 10% calibration slice of the dataset. We align directions so that larger values always indicate better performance (i.e. Cumulative Change was sign-inverted). We then normalize the correlations to obtain weights that sum to one, which yields an interpretable distribution of relative importance across metrics. The combined LT score for each sampled solution weighted sum of the LT values, where signals that are more strongly associated with accuracy on the calibration set contributed more the final score. Table reports the weights obtained.
Table 5: Metric weights for Combined Latent Space score.
Net Change
Cumulative Change
Aligned Change
Model
Dataset
R1-D
GPQA
0.35
0.40
0.25
PhiR+
GPQA
0.30
0.38
0.31
Qwen3
GPQA
0.32
R1-D
AIME2025
0.31
0.35
0.34
PhiR+
AIME2025
0.29
Qwen3
AIME2025
0.30
0.43
0.28
R1-D
TSP
0.24
0.39
0.37
PhiR+
TSP
0.20
0.38
0.42
Qwen3
TSP
0.19
0.43
0.37
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E LATENI-TRAJECTON SIGNALS FOR INFERENCE-TINE SCALING
In the main text, we evaluated efficiency and reliability when applying LT thresholds in a sequential inference procedure. Here, we provide additional analyses focusing on the subset of samples that exceeded the thresholds. This allows us to directly quantify (i) the accuracy of solutions accepted early and (ii) the proportion of datapoints where the LT decision rule was triggered.
Table reports accuracy and coverage for above-threshole samples across models and datasets. Ac- curacy here refers only to the subset of candidate solutions whose LT score surpassed the calibrated threshold, while the "Datapoints" column indicates the fraction of evaluation datapoints where an early stop occurred. As expected, above-threshold samples are consistently more accurate than the overall average, often approaching ceiling performance for stricter thresholds. At the same time, the coverage varies: some learned thresholds are more lenient, allowing rule to apply larger fraction of datapoints, while others are stricter, isolating a smaller but more reliable subset.
Table Above threshold evaluation across models and datasets with LT (LT) strategies. Accuracy (%) of samples above threshold, and percentage of Datapoints where LT decision rule was applied.
GPQA
AIME2025
TSP
Acc. (%)
Datapoints (%)
Acc.
Datapoints (%)
Acc.
Datapoints (%)
Model
Strategy
(%)
(%)
LT Net
64.60
88.2
68.73
90.5
28.73
99.4
LT Cumulative
67.57
81.4
75.63
54.0
32.13
96.4
RI-D
LT Aligned
63.77
92.0
73.00
82.5
31.60
93.4
LT Combined
66.50
81.2
78.53
65.1
30.53
98.5
LT Net
84.37
60.0
82.20
80.9
42.03
97.0
LT Cumulative
84.87
57.1
89.53
74.6
56.20
74.7
PhiR+
LT Aligned
88.10
44.6
89.33
71.4
49.73
88.7
LT Combined
86.30
48.0
91.10
69.8
51.53
81.5
LT Net
65.17
95.6
87.13
92.1
45.87
61.6
LT Cumulative
69.47
74.1
96.50
85.7
37.90
95.8
Qwen3
LT Aligned
64.73
87.7
85.37
95.2
40.50
87.5
LT Combined
65.53
88.2
85.37
95.2
47.43
74.4
To further illustrate this tradeoff, Figure 13 plots accuracy as function of threshold quantiles. Higher quantiles consistently yield higher accuracy across all metries, models, and datasets, indi- cating that L.T signals reliably concentrate correct solutions their upper ranges. several cases, accuracy at the top quantiles approaches 100%, demonstrating that filtering by strong LT signals isolates highly reliable reasoning traces.
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Metric
Net Change
Cumulative Change
Aligned Change
GPQA
AIME2025
TSP
20
3.9
Phi4+
Accuracy
3.5
0.
3.0
0.9
Accuracy
R1-D
885
8.00
Qwen3
0.1%
3.4
0.0
655
Quantile
Quantile
Quantile
Figure 13: Accuracy of datapoints above thresholds defined over a range quantiles. Given that Cumulative Change is negatively correlated with accuracy, we inverted the quantile selection (i.e., replacing q with 1 - q) so that higher quantiles consistently correspond to higher expected accuracy. For all metrics, accuracy increases consistently with higher quantiles across datasets and models. This evidences that these metrics are predictive answer quality.
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F COMPARISON AGAINST FIRST SAMPLE BASELINE
To further evaluate the robustness of our mutli-sample selection strategy, we compare against a simpler baseline where the first sample of each question is kept as the final choice.
As shown in Table 7 this heuristic is occasionally competitive, but also unreliable: while matches LT performance in few cases and is slightly higher in small number of settings, also suffers substantial drops in others (e.g. -6 to -10 points on some complex datasets). This variability confirms that outcome quality for early samples highly inconsistent across tasks and models, meaning there is no dependable way to know when the first or second sample will be much worse. In contrast, L-T-baee selection offers a principled and stable alternative. It uses information already produced during decoding (so computational cost is essentially unchanged), and it delivers consistently strong performance, often yielding notable gains on more challenging datasets. Even in cases where LT is slightly below the first-sample heuristic, the differences are small, whereas the improvements in the opposite direction can be large.
Table 7: Accuracy with Latent-Trajectory Trajectory (LT) signals. All accuracies are expressed in percent. "First Sample" uses first-sample baseline accuracies. A the accuracy gain relative to this baseline.
Model
Strategy
GPQA (Ace/ A) AIME2025 (Ace/ A)
TSP (Ace /A)
First Sample
60.41
56.67
28.13
LT-Net
61.10 (40.69)
61.90 (45.23)
28.60 (40.47)
RI-D
LT-Cumulative
62.10 (+1.69)
58.70 (42.03)
30.90 (42.77)
LT Aligned
61.10 (+0.69)
60.30 (+3.63)
29.50 (+1.37)
First Sample
69.70
73.33
43.13
IT-Net
68.80 (-0.90)
79.40 (46.07)
42.30 (-0.83)
Phi4R+
LT Cumulative
69.60 (-0.10)
81.00 (+7.67)
44.40 (+1.27)
LT [T-Aligned
69.60 (-0.10)
82.50 (+9.17)
44.10 (+0.97)
First Sample
63.13
76.67
36.25
LT-Net
63.70 (40.57)
79.40 (+2.73)
35,40 (-0.85)
Qwen3
LT IB-Cumllative
63.30 (+0.17)
84.10 (+7.43)
36.30 (40.05)
LT-Aligned
64.20 (+1.07)
80.90 (+4.233
37,80 (+1.55)
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G CROSS-DATASET GENERALIZATION IN SEQUENTIAL STRATEGIES
To assess whether LT thresholding requires dataset-specific tuning or can generalize across reason- ing tasks, we conduct cross-datasct threshold transfer experiment. Our thresholding mechanism is percentile-base:: cross-validation selects the optimal percentile of the combined LT score distribu- tion rather than fixed numeric value. This percentile is then mapped each dataset's distribution at evaluation time.
For each model, we perform the following procedure: (i) Use voos-validation on one dataset (e.g., GPQA) to select the optimal Combined LTT score percentile; (ii) Apply the same percentile, without re-tuning, to all other datasets. Each test dataset computes the corresponding numeric threshold us- ing its own metric distribution; (iii) Repeat for all train-test dataset pairs across GPQA, AIME2025, and TSP. Reported values the table are averaged over the test datasets for each model.
The results (see Table () show that single global percentile per model generalizes well: accuracy remains close to MV @5 or improves, while LT-CCominedd strategy consistently reduces sample usage by 20-70%. This demonstrates that LT- based thresholding does not rely on dataset-specific calibration and maintains strong performance when transferred across heterogeneous reasoning do- mains.
Table 8: cooss-dtaset threshold generalizability.
GPQA
AIME2025
TSP
Model
Strategy
(avg % AAoc)
Samples (avg Tok %))
(avg & Acc)
Samples (avg ATok %)
(avg % (AAc))
Acc.
Samples (avg. ATak %)
MV@5
59.90
56.67
RI-D
LT - Combined
60.40 (40.5)
2.11 (+41.5)
60.00 (+3.3)
1.40 (+60.3)
28.45 (+1.0)
1.59 (+67.3)
Phi4R+
MV@5
70.20
5,00
80.00
5.00
41.25
5.00
LT - Combined
69.20 (-1.0)
2.64 (425.5)
80.00 (+0.0)
2.87 (+23.1)
41.85 (+0.6)
2.52 (+15.2)
Qwen3
MV@5
63.96
5,00
70.00
5.00
36.25
5.00
IT Combined
63.60 (-0.4)
2,08 (445.4)
76.65 (46.7)
2.18 (+40.5)
35.90
(-0.3)
1.29 (+73.6)
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H CROSS-DATASET GENERALIZATION IN EARLY EXIT STRATEGIES
To evaluate whether early in the trace exit experiments can be generalized across reasoning tasks, we train a random forest classifier from multiple datasets and evaluate it using leave-one-datasetoout protocol. Concretely, for each model, we pool the LT signals from two datasets and train a Random Forest classifier on this combined set, using only the early-step features. The third dataset is held out entirely and used solely for evaluation. At test time, the trained Random Forest is applied to the held-out dataset to score partial chains of thought and select the predicted best reasoning path. This design directly measures cross-dataset transfer: the selector must generalize to reasoning domain has never seen, demonstrating that early-trace selection does not require dataset-specific tuning.
reports the averaged values of the cross-datase classifier accuracies on the first 4 reasoning segments (composed of 500 tokens). Results show that LT-based early trace selection generalizes well across datasets: classifier trained on two datasets maintains competitive accuracy the held- out dataset while saving 60-75% tokens. Despite having no access to task-specific labels, LT matches exceeds the Maj@5 baseline in several settings, demonstrating that early- signals are transferable across heterogeneous reasoning tasks. Accuracy differences remain small (typically within +2%), confirming that strong cross-dataset performance be achieved without dataset- specific tuning.
Table 9: Cross Coos--Dtasett generalizability early in the trace.
GPQA
AIME2025
TSP
Accuracy (% / A%)
Saved Tokens (A%)
Accuracy (% / A%)
Saved Tokens (A%)
Accuracy (% / A%)
Saved Tokens (A%)
Model
Strategy
Maj@5
59.90
56.67 56.67 (+0.0)
27.5 30.78 (+3.3)
RI-D
LT
60.91 (+1.0)
+59.7
+59.2
+69.4
Maj@5
70.20
80.00
41.25
PhiR+
LT
69.07 (-1.1)
+70.4
82.50 (+2.5)
+71.3
40,00 (-1.3)
+75.1
Maj@5
63.96
70.00
36.25
Qwen3
LT
62.31 (-1.7)
+60.7
73.33 (+3.3)
+73.4
37.03 (+0.8)
+71.1
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I ACCURACY ACROSS EARLY STEPS
To examine how early checkpoint selection affects the robustness of our metric, we evaluate the accuracy of the LT-based approach checkpoints taken every 500 tokens over the first 2500 tokens of each trace.
As shown in Figure 14 LT-based accuracy remains both significant and stable across early check- point choices. The average variation across segments is 0.05.
GPOA
NNME2025
TSP
0.450
0.73
0.02
0.375
2.10
0.350
0.64
0.66
0.02
0.75
1500
2500
Number of
Number tobenn
Figure 14: Latent- tilient-rraectory--rr based accuracies across early reasoning segments.
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J REPRESENTATIONAL AVERAGING
To enhance the signal robustness and reduce the dimensionality of the reasoning trace, we partition into non-overlapping reasoning segments of size 500. For each layer, then average the token representations within each segment. We found that this procedure preserves the overall trajectory of the reasoning process.
The choice of 500 tokens was guided by the average answer lengths across datasets. The dataset with the shortest responses still had an average of 5k tokens per answer. Setting the window 500 tokens therefore ensures that, on average, we obtain at least 10 measurement points per answer in this dataset, and proportionally more in the others.
To further demonstrate that LT signals can still be predictive of shorter reasoning traces, Table T0 demonstrates how our ROC-AUC results are equivalent when considering interval segments of other sizes. For each LT signal, the fluctuations across segment lengths are overall small (with an average variation across segment lengths 0.01 0.05 for a model-dataset pair).
Model
Dataset
Net Change
Cumulative Change
Aligned
Change
100
300
500
700
100
300
500
700
100
300
500
700
GPQA
0.69
0.69
0.69
0.68
0.71
0.70
0.69
0.68
0.71
0.68
0.67
0.66
RI-D
AIME2025
0.70
0.74
0.76
0.76
0.73
0.72
0.70
0.71
0.72
0.75
0.76
0.74
TSP
0.64
0.61
0.64
0.66
0.69
0.69
0.69
0.69
0.57
0.64
0.66
0.68
GPQA
0.75
0.74
0.74
0.75
0.75
0.74
0.74
0.74
0.75
0.74
0.74
0.73
Phi4R+
AIME2025
0.78
0.77
0.75
0.79
0.80
0.79
0.79
0.78
0.80
0.77
0.76
0.77
TSP
0.71
0.67
0.62
0.71
0.79
0.78
0.78
0.78
0.72
0.68
0.73
0.75
GPQA
0.67
0.67
0.67
0.65
0.69
0.67
0.66
0.65
0.69
0.66
0.63
0.62
Qwen3
AIME2025
0.94
0.93
0.92
0.92
0.95
0.95
0.95
0.95
0.91
0.93
0,91
0.92
TSP
0.67
0.63
0.64
0.63
0.71
0.74
0.75
0.76
0.53
0.65
0.68
0.70
Table 10: AUC-ROC results across segment lengths
In addition, we compared fixed-k: strategy to defining segments by newline tokens, as other studies report (Sun al.) 2025). However, segment sizes varied substantially across models under this approach, making it less comparable across architectures.
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K MODELS AND INFERENCE SETTINGS
We perform our inference and evaluation using the Eureka ML microsoft/eureka m. m1-insights.
Insights
framework
We used a max generation length of 31,768 tokens for all models. For all experiments, we report reproducibility:
model sources and inference parameters
De--Seek-RI-Qwen 14B
[eepppakee//bbesesa----.a--dae-1 -
Temperature = 0.6
Top-p=0.95
Thi-4-Reasoning-PPH
Temperature = 0.8
Top-k = 50
Top-p=0.95
Qwen3 14B (thinking enabled)
Swen/Qwen3-14B
Temperature = 0.6
Top-p = 0.95
Top-k 20
PeepSeck-RI-Laamee 8B
deepseek Rl 8B
Temperature = 0.6
00p-p=0.95
DeepSeek-R1-Qwee 32B
Temperature = 0.6
Top-p 0.95
SeepSeck-Rl-Lanne 70B
aickaaai//eeeeee Banteeekk-R--Da- R1-Distill -1 ama 70B
Temperature = 0.6
Top-p = 0.95
32


Reviews:
[{"review_id": "fxKm9rJhUd", "reviewer": "Reviewer_AG1e", "summary": "This paper introduces a method for predicting the correctness of a reasoning path by analyzing the direction and magnitude of change in latent vectors over the temporal dimension. The goal is to infer the quality of a reasoning path in advance by examining its latent dynamics during the intermediate generation process. Experiments demonstrate that the proposed Latent-Trajectory (LT) signals correlate with final answer accuracy. Furthermore, the paper shows that these LT signals can be practically applied for early answer selection and path pruning, significantly reducing the number of tokens generated while maintaining, and in some cases improving, overall accuracy.", "questions": "1. For the \"Early Answer Selection\" experiment, how does the LT signal-based stopping rule compare to simpler serial baselines? For instance, what is the accuracy if one simply stops after the first sample (k=1) or the second (k=2)? A key concern is that the tested datasets might have low output variance, and a simple k=1 or k=2 rule might achieve similar results without the complexity of calculating LT signals.\n\n2. The method's reliance on calibration on a labeled (correct/incorrect) dataset seems to limit its application. How do the authors envision this method being practiced in more general, open-ended scenarios (e.g., creative writing, general instruction-following) where there is no single ground truth and \"correct/incorrect\" labels for calibration are unavailable?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. Training-free method: The core method is training-free and does not rely on semantic understanding. It provides a novel way to discriminate reasoning quality based purely on the internal dynamics of the model's hidden states, making it a lightweight and potentially generalizable approach.\n\n2. Positive experimental results: The experiments successfully show a positive correlation between the proposed LT signals and final answer accuracy. More importantly, the paper demonstrates the practical utility of these signals in two efficiency-oriented applications (Early Answer Selection and Early Path Selection), achieving notable reductions in number of tokens while preserving or even slightly improving accuracy .\n\n3. Novel methodological perspective: The central idea of analyzing the temporal evolution of latent states is a novel contribution. It offers a new lens for understanding the reasoning process.", "weaknesses": "1. Moderate correlation and robustness: The reported correlations (Spearman's r in Table 3) are moderate, often falling in the 0.3-0.7 range. While the authors show this is sufficient for the tested datasets, this moderate correlation suggests the signal is somewhat noisy and raises concerns about the method's robustness when applied to different models, domains, or tasks.\n\n2. Heavy reliance on unjustified hyperparameters: The methodology is built on several ad-hoc, empirical settings that lack strong theoretical or intuitive justification. For example, segmentation (k=500), averaging (across-layer), or checkpoint (t=2000) in early pruning experiment. This work does not provide a guide on how to set this parameters when applied to other tasks.\n\n3. Missing Comparison to Key Related Work: A critical weakness is the lack of experimental comparison to highly relevant, concurrent work. Specifically, the paper does not compare ST-BoN [1], which shares an identical motivation (efficient Best-of-N) but uses a competing approach (spatial/layer-wise trajectory analysis). Given the strong overlap in goals, a direct comparison is necessary to properly situate this work's contribution.\n\nReference:\n[1] Wang Y, Zhang P, Huang S, et al. Sampling-efficient test-time scaling: Self-estimating the best-of-n sampling in early decoding[J]. arXiv preprint arXiv:2503.01422, 2025.\n\n4. Inefficient experimental setup for Early Selection: The \"Early Answer Selection\" experiment relies on a serial generation strategy (generating samples one by one). This approach discards the parallel processing capabilities of modern inference infrastructure (i.e., batch decoding) and is likely highly inefficient in real-world applications.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "JMbJp2CMjE", "reviewer": "Reviewer_SL11", "summary": "The paper proposes novel methods for evaluating the reasoning chains of language models. The chain is split into segments of length 500. For each layer, the mean hidden state of these segments is calculated. The paper suggests three measures: (1) *net change* is the L2 norm of the difference between the hidden vectors of the first and the last segment; (2) *cumulative change* is the sum of the L2 norms of the differences between consecutive hidden vectors ; and (3) *aligned change* is the average cosine similarity between each segment's update vector and the overall trend of the reasoning chain. For all measures, the mean across all layers is used to get a single scalar score. It is shown that high *net change* and high *aligned change* are associated with correct reasoning, whereas high *cumulative change* is associated with incorrect reasoning. These correlations are stronger than previously proposed measures. The paper shows how this allows for better selection between several alternative reasoning chains and even early selection of promising reasoning paths, leading to higher accuracy and efficiency.", "questions": "- Lines 340-353: Is the cross-validation to select the threshold done separately for each benchmark (GPQA, AIME2025, TSP), or is the same threshold used for all benchmarks? The more meaningful and fair setting in my mind would be one where a global threshold would be chosen for a model and then used for all reasoning problems.\n- It makes intuitive sense that for many kinds of reasoning problems, a 'direct' or linear route through the latent space is desirable. However, other problems—particularly ones that require novel out-of-the-box solutions, lots of trial and error, divergent instead of convergent thinking —might benefit from less direct traces. How broad is the class of problems for which the presented results hold?\n- More of a comment: the authors might be interested in recent work analysing the hidden states of language models using, for example, the lens of prompt entropy (\"Layer by Layer: Uncovering Hidden Representations in Language Models\", Skean et al., 2025) or information gain (\"Measuring In-Context Computation Complexity via Hidden State Prediction\", Herrmann et al., 2025).", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Clear description of the method: The paper clearly outlines the three Latent-Trajectory signals and how they are computed .\n- Thorough experimental evaluation: The method is tested across multiple modern reasoning models and three distinct domains (science, math, and algorithmic problems), demonstrating robust findings .\n- Immediately useful and practical results: The paper demonstrates that the proposed method can improve both the accuracy and efficiency of reasoning models. A practical advantage is that the method is training-free, it does not require costly annotations or the training of additional verifier models, making it simple to implement.", "weaknesses": "The proposed methods for improving performance and efficiency need some parameter tuning (e.g., threshold calibration). The segment size over which the averaged hidden states are computed is fixed at 500 (a segment size of 300 was also tested ). More fine-grained tracking of the hidden states might be insightful.\n\nThese are only minor concerns, which lead to my current vote of weak accept. If my questions below are addressed, I would consider further increasing my score.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "vnbE40MVXz", "reviewer": "Reviewer_NXfb", "summary": "In this work, the paper explores whether through intermediate activations we can identify chains of thought that are unlikely to lead to a correct answer and, if so, leverage these signals to guide the selection of more promising reasoning paths. They explore a number of signals, namely Net Change (measuring the overall magnitude of representational drift from initial to final hidden states), Cumulative Change (quantifying the total accumulated movement across intermediate updates), and Aligned Change (assessing how well sequential updates progress toward the final state via cosine similarity), and first investigate their correlation with accuracy. They find the signals can predict solution correctness across various science, math, and algorithmic benchmarks on several reasoning models, consistently outperforming baselines like cross-layer signals and output-distribution metrics. They use this to improve performance by guiding multi-sample inference through early answer selection and trace pruning, substantially reducing token usage while preserving or boosting accuracy on average over majority voting. The signals emerge early enough to allocate compute dynamically to promising paths.", "questions": "1. **Comparison to Reinforcement Learning Training**: Why pursue this inference-time heuristic approach using Latent-Trajectory signals when reinforcement learning methods like GRPO could directly train models to favor productive reasoning paths during optimization, potentially eliminating unproductive traces without the need for handcrafted, post-training interventions that may not generalize across diverse architectures?\n\n2. **Causal Interpretability**: While LT signals correlate with accuracy, do they reveal causal mechanisms in reasoning (e.g., via interventions on hidden states), or are they just correlated signals?\n\n3. What evidence is there that such trace signals will generalise to other model classes and sizes and on different problems", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 1, "strengths": "1. **Training-Free Methodology**: The paper introduces Latent-Trajectory (LT) signals (Net Change, Cumulative Change, and Aligned Change) that leverage hidden states during reasoning without requiring additional training, annotations, or external models, making it highly practical for deployment across diverse LLMs.\n\n2. **Superior Predictive Performance**: LT signals demonstrate robust discriminative power, achieving ROC-AUC scores of 0.71–0.74 for predicting solution accuracy across multiple datasets (GPQA, AIME 2025, TSP) and models (DeepSeek-R1-Distill-Qwen-14B, Phi4-Reasoning-Plus, Qwen3-14B), consistently outperforming baselines like cross-layer signals and output-distribution metrics.\n\n3. **Efficiency Gains in Inference Scaling**: By enabling early answer selection and trace pruning, LT signals reduce token usage by up to 70% in multi-sample inference, addressing key challenges in compute-intensive reasoning tasks like overthinking.", "weaknesses": "1.  **Heuristic Approach with Unproven Scalability**: The proposed Latent-Trajectory signals are fundamentally heuristic, derived from correlations observed in a narrow set of models. The evaluation is restricted to three models of a similar size from only two architectural families (Qwen and Phi). This lack of extensive analysis across different model types and, crucially, different model sizes (e.g., 7B, 70B, or larger) makes it unclear if these signals are a fundamental property of scaled reasoning or an emergent artifact of the specific models tested. Without this verification, there are significant questions about whether the method will \"survive scaling\" and generalize to future, more capable architectures.\n\n2.  **Insufficient Discussion of Training Alternatives**: The work positions itself as a necessary inference-time intervention without adequately addressing why modern training methods, such as reinforcement learning (e.g., GRPO-style approaches), are not already solving this problem. A key question left unanswered is why models cannot be trained to inherently favor productive reasoning strategies, which would be a more robust and fundamental solution than applying a post-hoc heuristic. This omission weakens the justification for needing such an inference-time fix.\n\n3.  **Narrow Domain Scope**: The experiments are confined to three specific and relatively structured reasoning domains: scientific multiple-choice (GPQA), contest math (AIME 2025), and algorithmic optimization (TSP). The paper does not evaluate the signals on more diverse or open-ended tasks, such as commonsense reasoning, creative problem-solving, or multi-hop question answering over text. This narrow scope limits the claim that LT signals are a general-purpose tool for assessing reasoning quality across the broad spectrum of tasks LLMs are applied to.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)