Orionnnn/hub-stats huggingface dataset:
Linear Correlation in LM's Compositional Generalization and
  Hallucination
Authors: name: Letian Peng, name: Chenyang An, name: Shibo Hao, name: Chengyu Dong, name: Jingbo Shang
Published: 2025-02-06 21:44:30

The generalization of language models (LMs) is undergoing active debates,
contrasting their potential for general intelligence with their struggles with
basic knowledge composition (e.g., reverse/transition curse). This paper
uncovers the phenomenon of linear correlations in LMs during knowledge
composition. For explanation, there exists a linear transformation between
certain related knowledge that maps the next token prediction logits from one
prompt to another, e.g., "X lives in the city of" rightarrow "X lives in the
country of" for every given X. This mirrors the linearity in human knowledge
composition, such as Paris rightarrow France. Our findings indicate that the
linear transformation is resilient to large-scale fine-tuning, generalizing
updated knowledge when aligned with real-world relationships, but causing
hallucinations when it deviates. Empirical results suggest that linear
correlation can serve as a potential identifier of LM's generalization.
Finally, we show such linear correlations can be learned with a single
feedforward network and pre-trained vocabulary representations, indicating LM
generalization heavily relies on the latter.
Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth
  Approach
Authors: name: Jonas Geiping, name: Sean McLeish, name: Neel Jain, name: John Kirchenbauer, name: Siddharth Singh, name: Brian R. Bartoldson, name: Bhavya Kailkhura, name: Abhinav Bhatele, name: Tom Goldstein
Published: 2025-02-07 18:55:02

We study a novel language model architecture that is capable of scaling
test-time computation by implicitly reasoning in latent space. Our model works
by iterating a recurrent block, thereby unrolling to arbitrary depth at
test-time. This stands in contrast to mainstream reasoning models that scale up
compute by producing more tokens. Unlike approaches based on chain-of-thought,
our approach does not require any specialized training data, can work with
small context windows, and can capture types of reasoning that are not easily
represented in words. We scale a proof-of-concept model to 3.5 billion
parameters and 800 billion tokens. We show that the resulting model can improve
its performance on reasoning benchmarks, sometimes dramatically, up to a
computation load equivalent to 50 billion parameters.
Generating Symbolic World Models via Test-time Scaling of Large Language
  Models
Authors: name: Zhouliang Yu, name: Yuhuan Yuan, name: Tim Z. Xiao, name: Fuxiang Frank Xia, name: Jie Fu, name: Ge Zhang, name: Ge Lin, name: Weiyang Liu
Published: 2025-02-07 07:52:25

Solving complex planning problems requires Large Language Models (LLMs) to
explicitly model the state transition to avoid rule violations, comply with
constraints, and ensure optimality-a task hindered by the inherent ambiguity of
natural language. To overcome such ambiguity, Planning Domain Definition
Language (PDDL) is leveraged as a planning abstraction that enables precise and
formal state descriptions. With PDDL, we can generate a symbolic world model
where classic searching algorithms, such as A*, can be seamlessly applied to
find optimal plans. However, directly generating PDDL domains with current LLMs
remains an open challenge due to the lack of PDDL training data. To address
this challenge, we propose to scale up the test-time computation of LLMs to
enhance their PDDL reasoning capabilities, thereby enabling the generation of
high-quality PDDL domains. Specifically, we introduce a simple yet effective
algorithm, which first employs a Best-of-N sampling approach to improve the
quality of the initial solution and then refines the solution in a fine-grained
manner with verbalized machine learning. Our method outperforms o1-mini by a
considerable margin in the generation of PDDL domain, achieving over 50%
success rate on two tasks (i.e., generating PDDL domains from natural language
description or PDDL problems). This is done without requiring additional
training. By taking advantage of PDDL as state abstraction, our method is able
to outperform current state-of-the-art methods on almost all competition-level
planning tasks.
Agency Is Frame-Dependent
Authors: name: David Abel, name: André Barreto, name: Michael Bowling, name: Will Dabney, name: Shi Dong, name: Steven Hansen, name: Anna Harutyunyan, name: Khimya Khetarpal, name: Clare Lyle, name: Razvan Pascanu, name: Georgios Piliouras, name: Doina Precup, name: Jonathan Richens, name: Mark Rowland, name: Tom Schaul, name: Satinder Singh
Published: 2025-02-06 08:34:57

Agency is a system's capacity to steer outcomes toward a goal, and is a
central topic of study across biology, philosophy, cognitive science, and
artificial intelligence. Determining if a system exhibits agency is a
notoriously difficult question: Dennett (1989), for instance, highlights the
puzzle of determining which principles can decide whether a rock, a thermostat,
or a robot each possess agency. We here address this puzzle from the viewpoint
of reinforcement learning by arguing that agency is fundamentally
frame-dependent: Any measurement of a system's agency must be made relative to
a reference frame. We support this claim by presenting a philosophical argument
that each of the essential properties of agency proposed by Barandiaran et al.
(2009) and Moreno (2018) are themselves frame-dependent. We conclude that any
basic science of agency requires frame-dependence, and discuss the implications
of this claim for reinforcement learning.
Step Back to Leap Forward: Self-Backtracking for Boosting Reasoning of
  Language Models
Authors: name: Xiao-Wen Yang, name: Xuan-Yi Zhu, name: Wen-Da Wei, name: Ding-Chu Zhang, name: Jie-Jing Shao, name: Zhi Zhou, name: Lan-Zhe Guo, name: Yu-Feng Li
Published: 2025-02-06 08:52:43

The integration of slow-thinking mechanisms into large language models (LLMs)
offers a promising way toward achieving Level 2 AGI Reasoners, as exemplified
by systems like OpenAI's o1. However, several significant challenges remain,
including inefficient overthinking and an overreliance on auxiliary reward
models. We point out that these limitations stem from LLMs' inability to
internalize the search process, a key component of effective reasoning. A
critical step toward addressing this issue is enabling LLMs to autonomously
determine when and where to backtrack, a fundamental operation in traditional
search algorithms. To this end, we propose a self-backtracking mechanism that
equips LLMs with the ability to backtrack during both training and inference.
This mechanism not only enhances reasoning ability but also efficiency by
transforming slow-thinking processes into fast-thinking through
self-improvement. Empirical evaluations demonstrate that our proposal
significantly enhances the reasoning capabilities of LLMs, achieving a
performance gain of over 40 percent compared to the optimal-path supervised
fine-tuning method. We believe this study introduces a novel and promising
pathway for developing more advanced and robust Reasoners.
VideoRoPE: What Makes for Good Video Rotary Position Embedding?
Authors: name: Xilin Wei, name: Xiaoran Liu, name: Yuhang Zang, name: Xiaoyi Dong, name: Pan Zhang, name: Yuhang Cao, name: Jian Tong, name: Haodong Duan, name: Qipeng Guo, name: Jiaqi Wang, name: Xipeng Qiu, name: Dahua Lin
Published: 2025-02-07 18:56:04

While Rotary Position Embedding (RoPE) and its variants are widely adopted
for their long-context capabilities, the extension of the 1D RoPE to video,
with its complex spatio-temporal structure, remains an open challenge. This
work first introduces a comprehensive analysis that identifies four key
characteristics essential for the effective adaptation of RoPE to video, which
have not been fully considered in prior work. As part of our analysis, we
introduce a challenging V-NIAH-D (Visual Needle-In-A-Haystack with Distractors)
task, which adds periodic distractors into V-NIAH. The V-NIAH-D task
demonstrates that previous RoPE variants, lacking appropriate temporal
dimension allocation, are easily misled by distractors. Based on our analysis,
we introduce VideoRoPE, with a 3D structure designed to
preserve spatio-temporal relationships. VideoRoPE features
low-frequency temporal allocation to mitigate periodic oscillations, a
diagonal layout to maintain spatial symmetry, and adjustable
temporal spacing to decouple temporal and spatial indexing. VideoRoPE
consistently surpasses previous RoPE variants, across diverse downstream tasks
such as long video retrieval, video understanding, and video hallucination. Our
code will be available at
https://github.com/Wiselnn570/VideoRoPE{https://github.com/Wiselnn570/VideoRoPE}.
CodeSteer: Symbolic-Augmented Language Models via Code/Text Guidance
Authors: name: Yongchao Chen, name: Yilun Hao, name: Yueying Liu, name: Yang Zhang, name: Chuchu Fan
Published: 2025-02-04 15:53:59

Existing methods fail to effectively steer Large Language Models (LLMs)
between textual reasoning and code generation, leaving symbolic computing
capabilities underutilized. We introduce CodeSteer, an effective method for
guiding LLM code/text generation. We construct a comprehensive benchmark
SymBench comprising 37 symbolic tasks with adjustable complexity and also
synthesize datasets of 12k multi-round guidance/generation trajectories and
5.5k guidance comparison pairs. We fine-tune the Llama-3-8B model with a newly
designed multi-round supervised fine-tuning (SFT) and direct preference
optimization (DPO). The resulting model, CodeSteerLLM, augmented with the
proposed symbolic and self-answer checkers, effectively guides the code/text
generation of larger models. Augmenting GPT-4o with CodeSteer raises its
average performance score from 53.3 to 86.4, even outperforming the existing
best LLM OpenAI o1 (82.7), o1-preview (74.8), and DeepSeek R1 (76.8) across all
37 tasks (28 seen, 9 unseen). Trained for GPT-4o, CodeSteer demonstrates
superior generalizability, providing an average 41.8 performance boost on
Claude, Mistral, and GPT-3.5. CodeSteer-guided LLMs fully harness symbolic
computing to maintain strong performance on highly complex tasks. Models,
Datasets, and Codes are available at
https://github.com/yongchao98/CodeSteer-v1.0.
ConceptAttention: Diffusion Transformers Learn Highly Interpretable
  Features
Authors: name: Alec Helbling, name: Tuna Han Salih Meral, name: Ben Hoover, name: Pinar Yanardag, name: Duen Horng Chau
Published: 2025-02-06 18:59:00

Do the rich representations of multi-modal diffusion transformers (DiTs)
exhibit unique properties that enhance their interpretability? We introduce
ConceptAttention, a novel method that leverages the expressive power of DiT
attention layers to generate high-quality saliency maps that precisely locate
textual concepts within images. Without requiring additional training,
ConceptAttention repurposes the parameters of DiT attention layers to produce
highly contextualized concept embeddings, contributing the major discovery that
performing linear projections in the output space of DiT attention layers
yields significantly sharper saliency maps compared to commonly used
cross-attention mechanisms. Remarkably, ConceptAttention even achieves
state-of-the-art performance on zero-shot image segmentation benchmarks,
outperforming 11 other zero-shot interpretability methods on the
ImageNet-Segmentation dataset and on a single-class subset of PascalVOC. Our
work contributes the first evidence that the representations of multi-modal DiT
models like Flux are highly transferable to vision tasks like segmentation,
even outperforming multi-modal foundation models like CLIP.
Weak-to-Strong Diffusion with Reflection
Authors: name: Lichen Bai, name: Masashi Sugiyama, name: Zeke Xie
Published: 2025-02-01 16:00:08

The goal of diffusion generative models is to align the learned distribution
with the real data distribution through gradient score matching. However,
inherent limitations in training data quality, modeling strategies, and
architectural design lead to inevitable gap between generated outputs and real
data. To reduce this gap, we propose Weak-to-Strong Diffusion (W2SD), a novel
framework that utilizes the estimated difference between existing weak and
strong models (i.e., weak-to-strong difference) to approximate the gap between
an ideal model and a strong model. By employing a reflective operation that
alternates between denoising and inversion with weak-to-strong difference, we
theoretically understand that W2SD steers latent variables along sampling
trajectories toward regions of the real data distribution. W2SD is highly
flexible and broadly applicable, enabling diverse improvements through the
strategic selection of weak-to-strong model pairs (e.g., DreamShaper vs. SD1.5,
good experts vs. bad experts in MoE). Extensive experiments demonstrate that
W2SD significantly improves human preference, aesthetic quality, and prompt
adherence, achieving SOTA performance across various modalities (e.g., image,
video), architectures (e.g., UNet-based, DiT-based, MoE), and benchmarks. For
example, Juggernaut-XL with W2SD can improve with the HPSv2 winning rate up to
90% over the original results. Moreover, the performance gains achieved by W2SD
markedly outweigh its additional computational overhead, while the cumulative
improvements from different weak-to-strong difference further solidify its
practical utility and deployability.
PlotGen: Multi-Agent LLM-based Scientific Data Visualization via
  Multimodal Feedback
Authors: name: Kanika Goswami, name: Puneet Mathur, name: Ryan Rossi, name: Franck Dernoncourt
Published: 2025-02-03 02:00:29

Scientific data visualization is pivotal for transforming raw data into
comprehensible visual representations, enabling pattern recognition,
forecasting, and the presentation of data-driven insights. However, novice
users often face difficulties due to the complexity of selecting appropriate
tools and mastering visualization techniques. Large Language Models (LLMs) have
recently demonstrated potential in assisting code generation, though they
struggle with accuracy and require iterative debugging. In this paper, we
propose PlotGen, a novel multi-agent framework aimed at automating the creation
of precise scientific visualizations. PlotGen orchestrates multiple LLM-based
agents, including a Query Planning Agent that breaks down complex user requests
into executable steps, a Code Generation Agent that converts pseudocode into
executable Python code, and three retrieval feedback agents - a Numeric
Feedback Agent, a Lexical Feedback Agent, and a Visual Feedback Agent - that
leverage multimodal LLMs to iteratively refine the data accuracy, textual
labels, and visual correctness of generated plots via self-reflection.
Extensive experiments show that PlotGen outperforms strong baselines, achieving
a 4-6 percent improvement on the MatPlotBench dataset, leading to enhanced user
trust in LLM-generated visualizations and improved novice productivity due to a
reduction in debugging time needed for plot errors.
Enhancing Code Generation for Low-Resource Languages: No Silver Bullet
Authors: name: Alessandro Giagnorio, name: Alberto Martin-Lopez, name: Gabriele Bavota
Published: 2025-01-31 12:23:28

The advent of Large Language Models (LLMs) has significantly advanced the
field of automated code generation. LLMs rely on large and diverse datasets to
learn syntax, semantics, and usage patterns of programming languages. For
low-resource languages (i.e., niche programming languages characterized by the
scarcity of training data), the limited availability of such data hampers the
models' ability to generalize effectively, resulting in poorer code generation
performance as compared to high-resource languages. For this reason, there is a
quest for techniques able to close this performance gap. We present an
empirical study investigating the effectiveness of several approaches for
boosting LLMs' performance on low-resource languages, namely: (i) a classic
fine-tuning, which is however capped in size by the scarcity of training data;
(ii) three variants of in-context learning, with prompts crafted to provide the
LLM with additional information about the low-resource language (e.g., few-shot
examples showcasing features of the targeted language); and (iii) a
pre-training objective teaching the model how to translate between high- and
low-resource languages. The context of our study are two low-resource languages
(R and Racket) and six LLMs having different architectures and sizes. Our
findings reveal that a fine-tuning is usually the best choice for smaller LLMs,
possibly due to the fact that even a small dataset is sufficient to train their
limited number of parameters. With the increase in size of the models,
in-context learning becomes more and more effective, representing a safe and
cheap bet (i.e., it always helps, but with different magnitudes). Differently,
very large LLMs may deteriorate their performance on low-resource languages
when fine-tuning is performed, possibly due to the lack of enough data needed
to effectively update their weights.
ChartCitor: Multi-Agent Framework for Fine-Grained Chart Visual
  Attribution
Authors: name: Kanika Goswami, name: Puneet Mathur, name: Ryan Rossi, name: Franck Dernoncourt
Published: 2025-02-03 02:00:51

Large Language Models (LLMs) can perform chart question-answering tasks but
often generate unverified hallucinated responses. Existing answer attribution
methods struggle to ground responses in source charts due to limited
visual-semantic context, complex visual-text alignment requirements, and
difficulties in bounding box prediction across complex layouts. We present
ChartCitor, a multi-agent framework that provides fine-grained bounding box
citations by identifying supporting evidence within chart images. The system
orchestrates LLM agents to perform chart-to-table extraction, answer
reformulation, table augmentation, evidence retrieval through pre-filtering and
re-ranking, and table-to-chart mapping. ChartCitor outperforms existing
baselines across different chart types. Qualitative user studies show that
ChartCitor helps increase user trust in Generative AI by providing enhanced
explainability for LLM-assisted chart QA and enables professionals to be more
productive.
Great Models Think Alike and this Undermines AI Oversight
Authors: name: Shashwat Goel, name: Joschka Struber, name: Ilze Amanda Auzina, name: Karuna K Chandra, name: Ponnurangam Kumaraguru, name: Douwe Kiela, name: Ameya Prabhu, name: Matthias Bethge, name: Jonas Geiping
Published: 2025-02-06 18:56:01

As Language Model (LM) capabilities advance, evaluating and supervising them
at scale is getting harder for humans. There is hope that other language models
can automate both these tasks, which we refer to as "AI Oversight". We study
how model similarity affects both aspects of AI oversight by proposing a
probabilistic metric for LM similarity based on overlap in model mistakes.
Using this metric, we first show that LLM-as-a-judge scores favor models
similar to the judge, generalizing recent self-preference results. Then, we
study training on LM annotations, and find complementary knowledge between the
weak supervisor and strong student model plays a crucial role in gains from
"weak-to-strong generalization". As model capabilities increase, it becomes
harder to find their mistakes, and we might defer more to AI oversight.
However, we observe a concerning trend -- model mistakes are becoming more
similar with increasing capabilities, pointing to risks from correlated
failures. Our work underscores the importance of reporting and correcting for
model similarity, especially in the emerging paradigm of AI oversight.
Speak Easy: Eliciting Harmful Jailbreaks from LLMs with Simple
  Interactions
Authors: name: Yik Siu Chan, name: Narutatsu Ri, name: Yuxin Xiao, name: Marzyeh Ghassemi
Published: 2025-02-06 18:59:02

Despite extensive safety alignment efforts, large language models (LLMs)
remain vulnerable to jailbreak attacks that elicit harmful behavior. While
existing studies predominantly focus on attack methods that require technical
expertise, two critical questions remain underexplored: (1) Are jailbroken
responses truly useful in enabling average users to carry out harmful actions?
(2) Do safety vulnerabilities exist in more common, simple human-LLM
interactions? In this paper, we demonstrate that LLM responses most effectively
facilitate harmful actions when they are both actionable and informative--two
attributes easily elicited in multi-step, multilingual interactions. Using this
insight, we propose HarmScore, a jailbreak metric that measures how effectively
an LLM response enables harmful actions, and Speak Easy, a simple multi-step,
multilingual attack framework. Notably, by incorporating Speak Easy into direct
request and jailbreak baselines, we see an average absolute increase of 0.319
in Attack Success Rate and 0.426 in HarmScore in both open-source and
proprietary LLMs across four safety benchmarks. Our work reveals a critical yet
often overlooked vulnerability: Malicious users can easily exploit common
interaction patterns for harmful intentions.
Analyze Feature Flow to Enhance Interpretation and Steering in Language
  Models
Authors: name: Daniil Laptev, name: Nikita Balagansky, name: Yaroslav Aksenov, name: Daniil Gavrilov
Published: 2025-02-05 09:39:34

We introduce a new approach to systematically map features discovered by
sparse autoencoder across consecutive layers of large language models,
extending earlier work that examined inter-layer feature links. By using a
data-free cosine similarity technique, we trace how specific features persist,
transform, or first appear at each stage. This method yields granular flow
graphs of feature evolution, enabling fine-grained interpretability and
mechanistic insights into model computations. Crucially, we demonstrate how
these cross-layer feature maps facilitate direct steering of model behavior by
amplifying or suppressing chosen features, achieving targeted thematic control
in text generation. Together, our findings highlight the utility of a causal,
cross-layer interpretability framework that not only clarifies how features
develop through forward passes but also provides new means for transparent
manipulation of large language models.
MAGA: MAssive Genre-Audience Reformulation to Pretraining Corpus
  Expansion
Authors: name: Xintong Hao, name: Ke Shen, name: Chenggang Li
Published: 2025-02-06 17:19:55

Despite the remarkable capabilities of large language models across various
tasks, their continued scaling faces a critical challenge: the scarcity of
high-quality pretraining data. While model architectures continue to evolve,
the natural language data struggles to scale up. To tackle this bottleneck, we
propose MAssive Genre-Audience~(MAGA) reformulation
method, which systematic synthesizes diverse, contextually-rich pretraining
data from existing corpus. This work makes three main contributions: (1) We
propose MAGA reformulation method, a lightweight and scalable approach for
pretraining corpus expansion, and build a 770B tokens MAGACorpus. (2) We
evaluate MAGACorpus with different data budget scaling strategies,
demonstrating consistent improvements across various model sizes (134M-13B),
establishing the necessity for next-generation large-scale synthetic
pretraining language models. (3) Through comprehensive analysis, we investigate
prompt engineering's impact on synthetic training collapse and reveal
limitations in conventional collapse detection metrics using validation losses.
Our work shows that MAGA can substantially expand training datasets while
maintaining quality, offering a reliably pathway for scaling models beyond data
limitations.