Orionnnn/hub-stats huggingface dataset:
Riddle Me This! Stealthy Membership Inference for Retrieval-Augmented
  Generation
Authors: name: Ali Naseh, name: Yuefeng Peng, name: Anshuman Suri, name: Harsh Chaudhari, name: Alina Oprea, name: Amir Houmansadr
Published: 2025-02-01 04:01:18

Retrieval-Augmented Generation (RAG) enables Large Language Models (LLMs) to
generate grounded responses by leveraging external knowledge databases without
altering model parameters. Although the absence of weight tuning prevents
leakage via model parameters, it introduces the risk of inference adversaries
exploiting retrieved documents in the model's context. Existing methods for
membership inference and data extraction often rely on jailbreaking or
carefully crafted unnatural queries, which can be easily detected or thwarted
with query rewriting techniques common in RAG systems. In this work, we present
Interrogation Attack (IA), a membership inference technique targeting documents
in the RAG datastore. By crafting natural-text queries that are answerable only
with the target document's presence, our approach demonstrates successful
inference with just 30 queries while remaining stealthy; straightforward
detectors identify adversarial prompts from existing methods up to ~76x more
frequently than those generated by our attack. We observe a 2x improvement in
TPR@1%FPR over prior inference attacks across diverse RAG configurations, all
while costing less than $0.02 per document inference.
Large Language Model Guided Self-Debugging Code Generation
Authors: name: Muntasir Adnan, name: Zhiwei Xu, name: Carlos C. N. Kuhn
Published: 2025-02-05 06:43:40

Automated code generation is gaining significant importance in intelligent
computer programming and system deployment. However, current approaches often
face challenges in computational efficiency and lack robust mechanisms for code
parsing and error correction. In this work, we propose a novel framework,
PyCapsule, with a simple yet effective two-agent pipeline and efficient
self-debugging modules for Python code generation. PyCapsule features
sophisticated prompt inference, iterative error handling, and case testing,
ensuring high generation stability, safety, and correctness. Empirically,
PyCapsule achieves up to 5.7% improvement of success rate on HumanEval, 10.3%
on HumanEval-ET, and 24.4% on BigCodeBench compared to the state-of-art
methods. We also observe a decrease in normalized success rate given more
self-debugging attempts, potentially affected by limited and noisy error
feedback in retention. PyCapsule demonstrates broader impacts on advancing
lightweight and efficient code generation for artificial intelligence systems.
On Teacher Hacking in Language Model Distillation
Authors: name: Daniil Tiapkin, name: Daniele Calandriello, name: Johan Ferret, name: Sarah Perrin, name: Nino Vieillard, name: Alexandre Ramé, name: Mathieu Blondel
Published: 2025-02-04 19:26:28

Post-training of language models (LMs) increasingly relies on the following
two stages: (i) knowledge distillation, where the LM is trained to imitate a
larger teacher LM, and (ii) reinforcement learning from human feedback (RLHF),
where the LM is aligned by optimizing a reward model. In the second RLHF stage,
a well-known challenge is reward hacking, where the LM over-optimizes the
reward model. Such phenomenon is in line with Goodhart's law and can lead to
degraded performance on the true objective. In this paper, we investigate
whether a similar phenomenon, that we call teacher hacking, can occur during
knowledge distillation. This could arise because the teacher LM is itself an
imperfect approximation of the true distribution. To study this, we propose a
controlled experimental setup involving: (i) an oracle LM representing the
ground-truth distribution, (ii) a teacher LM distilled from the oracle, and
(iii) a student LM distilled from the teacher. Our experiments reveal the
following insights. When using a fixed offline dataset for distillation,
teacher hacking occurs; moreover, we can detect it by observing when the
optimization process deviates from polynomial convergence laws. In contrast,
employing online data generation techniques effectively mitigates teacher
hacking. More precisely, we identify data diversity as the key factor in
preventing hacking. Overall, our findings provide a deeper understanding of the
benefits and limitations of distillation for building robust and efficient LMs.
Jailbreaking with Universal Multi-Prompts
Authors: name: Yu-Ling Hsu, name: Hsuan Su, name: Shang-Tse Chen
Published: 2025-02-03 08:44:24

Large language models (LLMs) have seen rapid development in recent years,
revolutionizing various applications and significantly enhancing convenience
and productivity. However, alongside their impressive capabilities, ethical
concerns and new types of attacks, such as jailbreaking, have emerged. While
most prompting techniques focus on optimizing adversarial inputs for individual
cases, resulting in higher computational costs when dealing with large
datasets. Less research has addressed the more general setting of training a
universal attacker that can transfer to unseen tasks. In this paper, we
introduce JUMP, a prompt-based method designed to jailbreak LLMs using
universal multi-prompts. We also adapt our approach for defense, which we term
DUMP. Experimental results demonstrate that our method for optimizing universal
multi-prompts outperforms existing techniques.
LayerTracer: Cognitive-Aligned Layered SVG Synthesis via Diffusion
  Transformer
Authors: name: Yiren Song, name: Danze Chen, name: Mike Zheng Shou
Published: 2025-02-03 06:49:58

Generating cognitive-aligned layered SVGs remains challenging due to existing
methods' tendencies toward either oversimplified single-layer outputs or
optimization-induced shape redundancies. We propose LayerTracer, a diffusion
transformer based framework that bridges this gap by learning designers'
layered SVG creation processes from a novel dataset of sequential design
operations. Our approach operates in two phases: First, a text-conditioned DiT
generates multi-phase rasterized construction blueprints that simulate human
design workflows. Second, layer-wise vectorization with path deduplication
produces clean, editable SVGs. For image vectorization, we introduce a
conditional diffusion mechanism that encodes reference images into latent
tokens, guiding hierarchical reconstruction while preserving structural
integrity. Extensive experiments demonstrate LayerTracer's superior performance
against optimization-based and neural baselines in both generation quality and
editability, effectively aligning AI-generated vectors with professional design
cognition.
Token Assorted: Mixing Latent and Text Tokens for Improved Language
  Model Reasoning
Authors: name: DiJia Su, name: Hanlin Zhu, name: Yingchen Xu, name: Jiantao Jiao, name: Yuandong Tian, name: Qinqing Zheng
Published: 2025-02-05 15:33:00

Large Language Models (LLMs) excel at reasoning and planning when trained on
chainof-thought (CoT) data, where the step-by-step thought process is
explicitly outlined by text tokens. However, this results in lengthy inputs
where many words support textual coherence rather than core reasoning
information, and processing these inputs consumes substantial computation
resources. In this work, we propose a hybrid representation of the reasoning
process, where we partially abstract away the initial reasoning steps using
latent discrete tokens generated by VQ-VAE, significantly reducing the length
of reasoning traces. We explore the use of latent trace abstractions in two
scenarios: 1) training the model from scratch for the Keys-Finding Maze
problem, 2) fine-tuning LLMs on this hybrid data with an extended vocabulary
including unseen latent tokens, for both logical and mathematical reasoning
problems. To facilitate effective learning, we introduce a simple training
procedure that randomly mixes latent and text tokens, which enables fast
adaptation to new latent tokens. Our approach consistently outperforms the
baselines methods in various benchmarks.
LIMO: Less is More for Reasoning
Authors: name: Yixin Ye, name: Zhen Huang, name: Yang Xiao, name: Ethan Chern, name: Shijie Xia, name: Pengfei Liu
Published: 2025-02-05 17:23:45

We present a fundamental discovery that challenges our understanding of how
complex reasoning emerges in large language models. While conventional wisdom
suggests that sophisticated reasoning tasks demand extensive training data
(>100,000 examples), we demonstrate that complex mathematical reasoning
abilities can be effectively elicited with surprisingly few examples. Through
comprehensive experiments, our proposed model LIMO demonstrates unprecedented
performance in mathematical reasoning. With merely 817 curated training
samples, LIMO achieves 57.1% accuracy on AIME and 94.8% on MATH, improving from
previous SFT-based models' 6.5% and 59.2% respectively, while only using 1% of
the training data required by previous approaches. LIMO demonstrates
exceptional out-of-distribution generalization, achieving 40.5% absolute
improvement across 10 diverse benchmarks, outperforming models trained on 100x
more data, challenging the notion that SFT leads to memorization rather than
generalization. Based on these results, we propose the Less-Is-More Reasoning
Hypothesis (LIMO Hypothesis): In foundation models where domain knowledge has
been comprehensively encoded during pre-training, sophisticated reasoning
capabilities can emerge through minimal but precisely orchestrated
demonstrations of cognitive processes. This hypothesis posits that the
elicitation threshold for complex reasoning is determined by two key factors:
(1) the completeness of the model's encoded knowledge foundation during
pre-training, and (2) the effectiveness of post-training examples as "cognitive
templates" that show the model how to utilize its knowledge base to solve
complex reasoning tasks. To facilitate reproducibility and future research in
data-efficient reasoning, we release LIMO as a comprehensive open-source suite
at https://github.com/GAIR-NLP/LIMO.
SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model
Authors: name: Loubna Ben Allal, name: Anton Lozhkov, name: Elie Bakouch, name: Gabriel Martín Blázquez, name: Guilherme Penedo, name: Lewis Tunstall, name: Andrés Marafioti, name: Hynek Kydlíček, name: Agustín Piqueres Lajarín, name: Vaibhav Srivastav, name: Joshua Lochner, name: Caleb Fahlgren, name: Xuan-Son Nguyen, name: Clémentine Fourrier, name: Ben Burtenshaw, name: Hugo Larcher, name: Haojun Zhao, name: Cyril Zakka, name: Mathieu Morlon, name: Colin Raffel, name: Leandro von Werra, name: Thomas Wolf
Published: 2025-02-04 21:43:16

While large language models have facilitated breakthroughs in many applications of artificial intelligence, their inherent largeness makes them computationally expensive and challenging to deploy in resource-constrained settings. In this paper, we document the development of SmolLM2, a state-of-the-art "small" (1.7 billion parameter) language model (LM). To attain strong performance, we overtrain SmolLM2 on ~11 trillion tokens of data using a multi-stage training process that mixes web text with specialized math, code, and instruction-following data. We additionally introduce new specialized datasets (FineMath, Stack-Edu, and SmolTalk) at stages where we found existing datasets to be problematically small or low-quality. To inform our design decisions, we perform both small-scale ablations as well as a manual refinement process that updates the dataset mixing rates at each stage based on the performance at the previous stage. Ultimately, we demonstrate that SmolLM2 outperforms other recent small LMs including Qwen2.5-1.5B and Llama3.2-1B. To facilitate future research on LM development as well as applications of small LMs, we release both SmolLM2 as well as all of the datasets we prepared in the course of this project.
A Probabilistic Inference Approach to Inference-Time Scaling of LLMs
  using Particle-Based Monte Carlo Methods
Authors: name: Isha Puri, name: Shivchander Sudalairaj, name: Guangxuan Xu, name: Kai Xu, name: Akash Srivastava
Published: 2025-02-03 18:50:50

Large language models (LLMs) have achieved significant performance gains via
scaling up model sizes and/or data. However, recent evidence suggests
diminishing returns from such approaches, motivating scaling the computation
spent at inference time. Existing inference-time scaling methods, usually with
reward models, cast the task as a search problem, which tends to be vulnerable
to reward hacking as a consequence of approximation errors in reward models. In
this paper, we instead cast inference-time scaling as a probabilistic inference
task and leverage sampling-based techniques to explore the typical set of the
state distribution of a state-space model with an approximate likelihood,
rather than optimize for its mode directly. We propose a novel inference-time
scaling approach by adapting particle-based Monte Carlo methods to this task.
Our empirical evaluation demonstrates that our methods have a 4-16x better
scaling rate over our deterministic search counterparts on various challenging
mathematical reasoning tasks. Using our approach, we show that
Qwen2.5-Math-1.5B-Instruct can surpass GPT-4o accuracy in only 4 rollouts,
while Qwen2.5-Math-7B-Instruct scales to o1 level accuracy in only 32 rollouts.
Our work not only presents an effective method to inference-time scaling, but
also connects the rich literature in probabilistic inference with
inference-time scaling of LLMs to develop more robust algorithms in future
work. Code and further information is available at
https://probabilistic-inference-scaling.github.io.
Demystifying Long Chain-of-Thought Reasoning in LLMs
Authors: name: Edward Yeo, name: Yuxuan Tong, name: Morry Niu, name: Graham Neubig, name: Xiang Yue
Published: 2025-02-05 17:13:32

Scaling inference compute enhances reasoning in large language models (LLMs),
with long chains-of-thought (CoTs) enabling strategies like backtracking and
error correction. Reinforcement learning (RL) has emerged as a crucial method
for developing these capabilities, yet the conditions under which long CoTs
emerge remain unclear, and RL training requires careful design choices. In this
study, we systematically investigate the mechanics of long CoT reasoning,
identifying the key factors that enable models to generate long CoT
trajectories. Through extensive supervised fine-tuning (SFT) and RL
experiments, we present four main findings: (1) While SFT is not strictly
necessary, it simplifies training and improves efficiency; (2) Reasoning
capabilities tend to emerge with increased training compute, but their
development is not guaranteed, making reward shaping crucial for stabilizing
CoT length growth; (3) Scaling verifiable reward signals is critical for RL. We
find that leveraging noisy, web-extracted solutions with filtering mechanisms
shows strong potential, particularly for out-of-distribution (OOD) tasks such
as STEM reasoning; and (4) Core abilities like error correction are inherently
present in base models, but incentivizing these skills effectively for complex
tasks via RL demands significant compute, and measuring their emergence
requires a nuanced approach. These insights provide practical guidance for
optimizing training strategies to enhance long CoT reasoning in LLMs. Our code
is available at: https://github.com/eddycmu/demystify-long-cot.
Boosting Multimodal Reasoning with MCTS-Automated Structured Thinking
Authors: name: Jinyang Wu, name: Mingkuan Feng, name: Shuai Zhang, name: Ruihan Jin, name: Feihu Che, name: Zengqi Wen, name: Jianhua Tao
Published: 2025-02-04 14:18:29

Multimodal large language models (MLLMs) exhibit impressive capabilities but
still face challenges in complex visual reasoning. While recent efforts attempt
to enhance MLLMs' reasoning by incorporating OpenAI o1-like structured thinking
through explicit search structures or teacher-guided distillation, they often
struggle to balance performance and efficiency. A critical limitation is their
heavy reliance on extensive data and search spaces, resulting in low-efficiency
implicit insight extraction and data utilization. To address this, we propose
AStar, an Automated Structured thinking paradigm for multimodal reasoning via
Monte Carlo Tree Search (MCTS). AStar automatically derives high-level
cognitive reasoning patterns from limited data using MCTS-powered hierarchical
structures. Building on these explicit patterns, we design a unified reasoning
framework that seamlessly integrates models' internal reasoning capabilities
and external reasoning guidelines, enabling efficient inference with minimal
tree iterations. This novel paradigm strikes a compelling balance between
performance and efficiency. Extensive experiments demonstrate AStar's
effectiveness, achieving superior accuracy (54.0%) on the MathVerse
benchmark with a 7B backbone, surpassing GPT-4o (50.2%) while maintaining
substantial data and computational efficiency.
TwinMarket: A Scalable Behavioral and Social Simulation for Financial
  Markets
Authors: name: Yuzhe Yang, name: Yifei Zhang, name: Minghao Wu, name: Kaidi Zhang, name: Yunmiao Zhang, name: Honghai Yu, name: Yan Hu, name: Benyou Wang
Published: 2025-02-03 16:39:48

The study of social emergence has long been a central focus in social
science. Traditional modeling approaches, such as rule-based Agent-Based Models
(ABMs), struggle to capture the diversity and complexity of human behavior,
particularly the irrational factors emphasized in behavioral economics.
Recently, large language model (LLM) agents have gained traction as simulation
tools for modeling human behavior in social science and role-playing
applications. Studies suggest that LLMs can account for cognitive biases,
emotional fluctuations, and other non-rational influences, enabling more
realistic simulations of socio-economic dynamics. In this work, we introduce
TwinMarket, a novel multi-agent framework that leverages LLMs to simulate
socio-economic systems. Specifically, we examine how individual behaviors,
through interactions and feedback mechanisms, give rise to collective dynamics
and emergent phenomena. Through experiments in a simulated stock market
environment, we demonstrate how individual actions can trigger group behaviors,
leading to emergent outcomes such as financial bubbles and recessions. Our
approach provides valuable insights into the complex interplay between
individual decision-making and collective socio-economic patterns.
Text-to-CAD Generation Through Infusing Visual Feedback in Large
  Language Models
Authors: name: Ruiyu Wang, name: Yu Yuan, name: Shizhao Sun, name: Jiang Bian
Published: 2025-01-31 11:28:16

Creating Computer-Aided Design (CAD) models requires significant expertise
and effort. Text-to-CAD, which converts textual descriptions into CAD
parametric sequences, is crucial in streamlining this process. Recent studies
have utilized ground-truth parametric sequences, known as sequential signals,
as supervision to achieve this goal. However, CAD models are inherently
multimodal, comprising parametric sequences and corresponding rendered visual
objects. Besides,the rendering process from parametric sequences to visual
objects is many-to-one. Therefore, both sequential and visual signals are
critical for effective training. In this work, we introduce CADFusion, a
framework that uses Large Language Models (LLMs) as the backbone and alternates
between two training stages: the sequential learning (SL) stage and the visual
feedback (VF) stage. In the SL stage, we train LLMs using ground-truth
parametric sequences, enabling the generation of logically coherent parametric
sequences. In the VF stage, we reward parametric sequences that render into
visually preferred objects and penalize those that do not, allowing LLMs to
learn how rendered visual objects are perceived and evaluated. These two stages
alternate throughout the training, ensuring balanced learning and preserving
benefits of both signals. Experiments demonstrate that CADFusion significantly
improves performance, both qualitatively and quantitatively.
Activation Approximations Can Incur Safety Vulnerabilities Even in
  Aligned LLMs: Comprehensive Analysis and Defense
Authors: name: Jiawen Zhang, name: Kejia Chen, name: Lipeng He, name: Jian Lou, name: Dan Li, name: Zunlei Feng, name: Mingli Song, name: Jian Liu, name: Kui Ren, name: Xiaohu Yang
Published: 2025-02-02 16:25:48

Large Language Models (LLMs) have showcased remarkable capabilities across
various domains. Accompanying the evolving capabilities and expanding
deployment scenarios of LLMs, their deployment challenges escalate due to their
sheer scale and the advanced yet complex activation designs prevalent in
notable model series, such as Llama, Gemma, and Mistral. These challenges have
become particularly pronounced in resource-constrained deployment scenarios,
where mitigating inference efficiency bottlenecks is imperative. Among various
recent efforts, activation approximation has emerged as a promising avenue for
pursuing inference efficiency, sometimes considered indispensable in
applications such as private inference. Despite achieving substantial speedups
with minimal impact on utility, even appearing sound and practical for
real-world deployment, the safety implications of activation approximations
remain unclear. In this work, we fill this critical gap in LLM safety by
conducting the first systematic safety evaluation of activation approximations.
Our safety vetting spans seven sota techniques across three popular categories,
revealing consistent safety degradation across ten safety-aligned LLMs.
Federated Sketching LoRA: On-Device Collaborative Fine-Tuning of Large
  Language Models
Authors: name: Wenzhi Fang, name: Dong-Jun Han, name: Liangqi Yuan, name: Seyyedali Hosseinalipour, name: Christopher G. Brinton
Published: 2025-01-31 18:44:35

Fine-tuning large language models (LLMs) on devices is attracting increasing
interest. Recent works have fused low-rank adaptation (LoRA) techniques with
federated fine-tuning to mitigate challenges associated with device model sizes
and data scarcity. Still, the heterogeneity of computational resources remains
a critical bottleneck: while higher-rank modules generally enhance performance,
varying device capabilities constrain LoRA's feasible rank range. Existing
approaches attempting to resolve this issue either lack analytical
justification or impose additional computational overhead, leaving a wide gap
for an efficient and theoretically-grounded solution. To address these
challenges, we propose federated sketching LoRA (FSLoRA), which leverages a
sketching mechanism to enable devices to selectively update submatrices of
global LoRA modules maintained by the server. By adjusting the sketching
ratios, which determine the ranks of the submatrices on the devices, FSLoRA
flexibly adapts to device-specific communication and computational constraints.
We provide a rigorous convergence analysis of FSLoRA that characterizes how the
sketching ratios affect the convergence rate. Through comprehensive experiments
on multiple datasets and LLM models, we demonstrate FSLoRA's superior
performance compared to various baselines.