Orionnnn/hub-stats huggingface dataset:
SR-Scientist: Scientific Equation Discovery With Agentic AI
Authors: name: Shijie Xia, name: Yuhan Sun, name: Pengfei Liu
Published: 2025-10-13 17:35:23

Recently, Large Language Models (LLMs) have been applied to scientific
equation discovery, leveraging their embedded scientific knowledge for
hypothesis generation. However, current methods typically confine LLMs to the
role of an equation proposer within search algorithms like genetic programming.
In this paper, we present SR-Scientist, a framework that elevates the LLM from
a simple equation proposer to an autonomous AI scientist that writes code to
analyze data, implements the equation as code, submits it for evaluation, and
optimizes the equation based on experimental feedback. Specifically, we wrap
the code interpreter into a set of tools for data analysis and equation
evaluation. The agent is instructed to optimize the equation by utilizing these
tools over a long horizon with minimal human-defined pipelines. Empirical
results show that SR-Scientist outperforms baseline methods by an absolute
margin of 6% to 35% on datasets covering four science disciplines.
Additionally, we demonstrate our method's robustness to noise, the
generalization of the discovered equations to out-of-domain data, and their
symbolic accuracy. Furthermore, we develop an end-to-end reinforcement learning
framework to enhance the agent's capabilities.
Detecting Data Contamination from Reinforcement Learning Post-training
  for Large Language Models
Authors: name: Yongding Tao, name: Tian Wang, name: Yihong Dong, name: Huanyu Liu, name: Kechi Zhang, name: Xiaolong Hu, name: Ge Li
Published: 2025-10-10 10:58:50

Data contamination poses a significant threat to the reliable evaluation of
Large Language Models (LLMs). This issue arises when benchmark samples may
inadvertently appear in training sets, compromising the validity of reported
performance. While detection methods have been developed for the pre-training
and Supervised Fine-Tuning stages, a critical research gap exists for the
increasingly significant phase of Reinforcement Learning (RL) post-training. As
RL post-training becomes pivotal for advancing LLM reasoning, the absence of
specialized contamination detection methods in this paradigm presents a
critical vulnerability. To address this, we conduct the first systematic study
of data detection within RL post-training scenario and propose Self-Critique.
Our method is motivated by a key observation: after RL phase, the output
entropy distribution of LLMs tends to collapse into highly specific and sparse
modes. Self-Critique probes for the underlying policy collapse, i.e., the
model's convergence to a narrow reasoning path, which causes this entropy
reduction. To facilitate this research, we also introduce RL-MIA, a benchmark
constructed to simulate this specific contamination scenario. Extensive
experiments show that Self-Critique significantly outperforms baseline methods
across multiple models and contamination tasks, achieving an AUC improvement of
up to 30%. Whereas existing methods are close to a random guess for RL-phase
contamination, our method makes detection possible.
Deconstructing Attention: Investigating Design Principles for Effective
  Language Modeling
Authors: name: Huiyin Xue, name: Nafise Sadat Moosavi, name: Nikolaos Aletras
Published: 2025-10-13 16:42:14

The success of Transformer language models is widely credited to their
dot-product attention mechanism, which interweaves a set of key design
principles: mixing information across positions (enabling multi-token
interactions), sequence-dependent activations (where attention weights adapt to
each input), a specific mathematical form (dot-product similarities plus
softmax weighting), and coupling of queries and keys to evolving hidden states
(grounding attention in the current layer). However, the necessity of each of
these principles remains largely untested. In this work, we systematically
deconstruct attention by designing controlled variants that selectively relax
these principles, applied both uniformly across all layers and in hybrid
architectures where only some layers retain standard attention. Our empirical
analysis reveals that mechanisms for mixing tokens are indispensable, as their
absence collapses models to near-random behavior, while the exact mathematical
form and sequence dependency can be substantially relaxed, especially when
preserved in just a subset of layers. Surprisingly, even variants that fail in
isolation can achieve robust performance when interleaved with standard
attention, highlighting a cooperative effect. These findings deepen our
understanding of what truly underpins attention's effectiveness and open new
avenues for simplifying language models without sacrificing performance.
dInfer: An Efficient Inference Framework for Diffusion Language Models
Authors: name: Yuxin Ma, name: Lun Du, name: Lanning Wei, name: Kun Chen, name: Qian Xu, name: Kangyu Wang, name: Guofeng Feng, name: Guoshan Lu, name: Lin Liu, name: Xiaojing Qi, name: Xinyuan Zhang, name: Zhen Tao, name: Haibo Feng, name: Ziyun Jiang, name: Ying Xu, name: Zenan Huang, name: Yihong Zhuang, name: Haokai Xu, name: Jiaqi Hu, name: Zhenzhong Lan, name: Junbo Zhao, name: Jianguo Li, name: Da Zheng
Published: 2025-10-09 16:19:42

Diffusion-based large language models (dLLMs) have emerged as a promising
alternative to autoregressive (AR) LLMs, leveraging denoising-based generation
to enable inherent parallelism. Even more and more open-sourced dLLM models
emerge, yet their widespread adoption remains constrained by the lack of a
standardized and efficient inference framework. We present dInfer, an efficient
and extensible framework for dLLM inference. dInfer decomposes the inference
pipeline into four modular components--model, diffusion iteration manager,
decoding strategy, and KV-cache manager--and integrates novel algorithms for
each component alongside system-level optimizations. Through this combination
of algorithmic innovations and system enhancements, dInfer achieves substantial
efficiency gains without compromising output quality on LLaDA-MoE. At batch
size 1, it surpasses 1,100 tokens per second on HumanEval and averages over 800
tokens per second across six benchmarks on 8times H800 GPUs. Compared to
prior systems, dInfer delivers a 10times speedup over Fast-dLLM while
maintaining similar model performance. Even compared to the AR model (with a
comparable number of activation parameters and performance) QWen2.5-3B, which
is highly optimized with the latest vLLM inference engine, dInfer still
delivers a 2-3times speedup. The implementation of dInfer is open-sourced
at https://github.com/inclusionAI/dInfer.
Deep Research Brings Deeper Harm
Authors: name: Shuo Chen, name: Zonggen Li, name: Zhen Han, name: Bailan He, name: Tong Liu, name: Haokun Chen, name: Georg Groh, name: Philip Torr, name: Volker Tresp, name: Jindong Gu
Published: 2025-10-13 19:05:00

Deep Research (DR) agents built on Large Language Models (LLMs) can perform
complex, multi-step research by decomposing tasks, retrieving online
information, and synthesizing detailed reports. However, the misuse of LLMs
with such powerful capabilities can lead to even greater risks. This is
especially concerning in high-stakes and knowledge-intensive domains such as
biosecurity, where DR can generate a professional report containing detailed
forbidden knowledge. Unfortunately, we have found such risks in practice:
simply submitting a harmful query, which a standalone LLM directly rejects, can
elicit a detailed and dangerous report from DR agents. This highlights the
elevated risks and underscores the need for a deeper safety analysis. Yet,
jailbreak methods designed for LLMs fall short in exposing such unique risks,
as they do not target the research ability of DR agents. To address this gap,
we propose two novel jailbreak strategies: Plan Injection, which injects
malicious sub-goals into the agent's plan; and Intent Hijack, which reframes
harmful queries as academic research questions. We conducted extensive
experiments across different LLMs and various safety benchmarks, including
general and biosecurity forbidden prompts. These experiments reveal 3 key
findings: (1) Alignment of the LLMs often fail in DR agents, where harmful
prompts framed in academic terms can hijack agent intent; (2) Multi-step
planning and execution weaken the alignment, revealing systemic vulnerabilities
that prompt-level safeguards cannot address; (3) DR agents not only bypass
refusals but also produce more coherent, professional, and dangerous content,
compared with standalone LLMs. These results demonstrate a fundamental
misalignment in DR agents and call for better alignment techniques tailored to
DR agents. Code and datasets are available at
https://chenxshuo.github.io/deeper-harm.
Bag of Tricks for Subverting Reasoning-based Safety Guardrails
Authors: name: Shuo Chen, name: Zhen Han, name: Haokun Chen, name: Bailan He, name: Shengyun Si, name: Jingpei Wu, name: Philip Torr, name: Volker Tresp, name: Jindong Gu
Published: 2025-10-13 16:16:44

Recent reasoning-based safety guardrails for Large Reasoning Models (LRMs),
such as deliberative alignment, have shown strong defense against jailbreak
attacks. By leveraging LRMs' reasoning ability, these guardrails help the
models to assess the safety of user inputs before generating final responses.
The powerful reasoning ability can analyze the intention of the input query and
will refuse to assist once it detects the harmful intent hidden by the
jailbreak methods. Such guardrails have shown a significant boost in defense,
such as the near-perfect refusal rates on the open-source gpt-oss series.
Unfortunately, we find that these powerful reasoning-based guardrails can be
extremely vulnerable to subtle manipulation of the input prompts, and once
hijacked, can lead to even more harmful results. Specifically, we first uncover
a surprisingly fragile aspect of these guardrails: simply adding a few template
tokens to the input prompt can successfully bypass the seemingly powerful
guardrails and lead to explicit and harmful responses. To explore further, we
introduce a bag of jailbreak methods that subvert the reasoning-based
guardrails. Our attacks span white-, gray-, and black-box settings and range
from effortless template manipulations to fully automated optimization. Along
with the potential for scalable implementation, these methods also achieve
alarmingly high attack success rates (e.g., exceeding 90% across 5 different
benchmarks on gpt-oss series on both local host models and online API
services). Evaluations across various leading open-source LRMs confirm that
these vulnerabilities are systemic, underscoring the urgent need for stronger
alignment techniques for open-sourced LRMs to prevent malicious misuse. Code is
open-sourced at https://chenxshuo.github.io/bag-of-tricks.
RAG-Anything: All-in-One RAG Framework
Authors: name: Zirui Guo, name: Xubin Ren, name: Lingrui Xu, name: Jiahao Zhang, name: Chao Huang
Published: 2025-10-14 09:25:35

Retrieval-Augmented Generation (RAG) has emerged as a fundamental paradigm
for expanding Large Language Models beyond their static training limitations.
However, a critical misalignment exists between current RAG capabilities and
real-world information environments. Modern knowledge repositories are
inherently multimodal, containing rich combinations of textual content, visual
elements, structured tables, and mathematical expressions. Yet existing RAG
frameworks are limited to textual content, creating fundamental gaps when
processing multimodal documents. We present RAG-Anything, a unified framework
that enables comprehensive knowledge retrieval across all modalities. Our
approach reconceptualizes multimodal content as interconnected knowledge
entities rather than isolated data types. The framework introduces dual-graph
construction to capture both cross-modal relationships and textual semantics
within a unified representation. We develop cross-modal hybrid retrieval that
combines structural knowledge navigation with semantic matching. This enables
effective reasoning over heterogeneous content where relevant evidence spans
multiple modalities. RAG-Anything demonstrates superior performance on
challenging multimodal benchmarks, achieving significant improvements over
state-of-the-art methods. Performance gains become particularly pronounced on
long documents where traditional approaches fail. Our framework establishes a
new paradigm for multimodal knowledge access, eliminating the architectural
fragmentation that constrains current systems. Our framework is open-sourced
at: https://github.com/HKUDS/RAG-Anything.
Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM
  Diversity
Authors: name: Jiayi Zhang, name: Simon Yu, name: Derek Chong, name: Anthony Sicilia, name: Michael R. Tomz, name: Christopher D. Manning, name: Weiyan Shi
Published: 2025-10-01 17:55:37

Post-training alignment often reduces LLM diversity, leading to a phenomenon
known as mode collapse. Unlike prior work that attributes this effect to
algorithmic limitations, we identify a fundamental, pervasive data-level
driver: typicality bias in preference data, whereby annotators systematically
favor familiar text as a result of well-established findings in cognitive
psychology. We formalize this bias theoretically, verify it on preference
datasets empirically, and show that it plays a central role in mode collapse.
Motivated by this analysis, we introduce Verbalized Sampling, a simple,
training-free prompting strategy to circumvent mode collapse. VS prompts the
model to verbalize a probability distribution over a set of responses (e.g.,
``Generate 5 jokes about coffee and their corresponding probabilities'').
Comprehensive experiments show that VS significantly improves performance
across creative writing (poems, stories, jokes), dialogue simulation,
open-ended QA, and synthetic data generation, without sacrificing factual
accuracy and safety. For instance, in creative writing, VS increases diversity
by 1.6-2.1x over direct prompting. We further observe an emergent trend that
more capable models benefit more from VS. In sum, our work provides a new
data-centric perspective on mode collapse and a practical inference-time remedy
that helps unlock pre-trained generative diversity.
R-WoM: Retrieval-augmented World Model For Computer-use Agents
Authors: name: Kai Mei, name: Jiang Guo, name: Shuaichen Chang, name: Mingwen Dong, name: Dongkyu Lee, name: Xing Niu, name: Jiarong Jiang
Published: 2025-10-13 19:52:04

Large Language Models (LLMs) can serve as world models to enhance agent
decision-making in digital environments by simulating future states and
predicting action outcomes, potentially eliminating costly trial-and-error
exploration. However, this capability is fundamentally limited by LLMs'
tendency toward hallucination and their reliance on static training knowledge,
which can lead to compounding errors that inhibit long-horizon simulations. To
systematically investigate whether LLMs are appropriate for world modeling, we
probe two core capabilities of world models--future state prediction and reward
estimation--through three tasks: next-state identification, full-procedure
planning alignment, and milestone transition recognition. Our analysis shows
that while LLMs effectively capture immediate next states and identify
meaningful state transitions, their performance rapidly degrades in
full-procedure planning. This highlights LLMs' limitations in reliably modeling
environment dynamics over long horizons. To address these limitations, we
propose the Retrieval-augmented World Model (R-WoM), which grounds LLM
simulations by incorporating factual, up-to-date knowledge retrieved from
external tutorials. Experiments show that R-WoM achieves substantial
improvements of up to 25.3% (OSWorld) and 18.1% (WebArena) compared to
baselines, with particular advantages in longer-horizon simulations.
SynthID-Image: Image watermarking at internet scale
Authors: name: Sven Gowal, name: Rudy Bunel, name: Florian Stimberg, name: David Stutz, name: Guillermo Ortiz-Jimenez, name: Christina Kouridi, name: Mel Vecerik, name: Jamie Hayes, name: Sylvestre-Alvise Rebuffi, name: Paul Bernard, name: Chris Gamble, name: Miklós Z. Horváth, name: Fabian Kaczmarczyck, name: Alex Kaskasoli, name: Aleksandar Petrov, name: Ilia Shumailov, name: Meghana Thotakuri, name: Olivia Wiles, name: Jessica Yung, name: Zahra Ahmed, name: Victor Martin, name: Simon Rosen, name: Christopher Savčak, name: Armin Senoner, name: Nidhi Vyas, name: Pushmeet Kohli
Published: 2025-10-10 11:03:31

We introduce SynthID-Image, a deep learning-based system for invisibly
watermarking AI-generated imagery. This paper documents the technical
desiderata, threat models, and practical challenges of deploying such a system
at internet scale, addressing key requirements of effectiveness, fidelity,
robustness, and security. SynthID-Image has been used to watermark over ten
billion images and video frames across Google's services and its corresponding
verification service is available to trusted testers. For completeness, we
present an experimental evaluation of an external model variant, SynthID-O,
which is available through partnerships. We benchmark SynthID-O against other
post-hoc watermarking methods from the literature, demonstrating
state-of-the-art performance in both visual quality and robustness to common
image perturbations. While this work centers on visual media, the conclusions
on deployment, constraints, and threat modeling generalize to other modalities,
including audio. This paper provides a comprehensive documentation for the
large-scale deployment of deep learning-based media provenance systems.
Tensor Logic: The Language of AI
Authors: name: Pedro Domingos
Published: 2025-10-14 08:24:08

Progress in AI is hindered by the lack of a programming language with all the
requisite features. Libraries like PyTorch and TensorFlow provide automatic
differentiation and efficient GPU implementation, but are additions to Python,
which was never intended for AI. Their lack of support for automated reasoning
and knowledge acquisition has led to a long and costly series of hacky attempts
to tack them on. On the other hand, AI languages like LISP an Prolog lack
scalability and support for learning. This paper proposes tensor logic, a
language that solves these problems by unifying neural and symbolic AI at a
fundamental level. The sole construct in tensor logic is the tensor equation,
based on the observation that logical rules and Einstein summation are
essentially the same operation, and all else can be reduced to them. I show how
to elegantly implement key forms of neural, symbolic and statistical AI in
tensor logic, including transformers, formal reasoning, kernel machines and
graphical models. Most importantly, tensor logic makes new directions possible,
such as sound reasoning in embedding space. This combines the scalability and
learnability of neural networks with the reliability and transparency of
symbolic reasoning, and is potentially a basis for the wider adoption of AI.
Information-Preserving Reformulation of Reasoning Traces for
  Antidistillation
Authors: name: Jiayu Ding, name: Lei Cui, name: Li Dong, name: Nanning Zheng, name: Furu Wei
Published: 2025-10-13 15:42:11

Recent advances in Large Language Models (LLMs) show that extending the
length of reasoning chains significantly improves performance on complex tasks.
While revealing these reasoning traces helps users better follow, verify, and
learn from the model's problem-solving process, it also makes them highly
vulnerable to unauthorized distillation. To mitigate this risk, proprietary
model providers often adopt aggressive protection strategies, such as replacing
detailed reasoning with brief summaries, which deprive users of valuable
intermediate information. To address this trade-off, we propose PART, an
information-preserving antidistillation reformulation of reasoning traces.
Motivated by the difference between how humans understand reasoning traces and
how LLMs exploit them for supervised fine-tuning, we design a simple but
effective two-step reformulation: removing self-talk behaviors and reordering
sub-conclusions. A small auxiliary model is trained to perform this
reformulation, incurring minimal computational overhead. Extensive experiments
demonstrate that PART consistently disrupts distillation across student models
of different sizes and types on various reasoning benchmarks. For instance,
when training on reformulated traces, even the performance of a large 32B
student model decreases from 54.17 to 46.88 on AIME 2024, corresponding to a
13.5% degradation.
ReFIne: A Framework for Trustworthy Large Reasoning Models with
  Reliability, Faithfulness, and Interpretability
Authors: name: Chung-En Sun, name: Ge Yan, name: Akshay Kulkarni, name: Tsui-Wei Weng
Published: 2025-10-10 07:08:44

Recent advances in long chain-of-thought (CoT) reasoning have largely
prioritized answer accuracy and token efficiency, while overlooking aspects
critical to trustworthiness. We argue that usable reasoning systems must be
trustworthy, characterized by three properties: interpretability, faithfulness,
and reliability. To this end, we propose ReFIne, a new training framework that
integrates supervised fine-tuning with GRPO to encourage models to: (i) improve
interpretability by producing structured, tag-based traces with high-level
planning that are easier for humans to follow; (ii) enhance faithfulness by
explicitly disclosing the decisive information guiding each solution, with
consistent cross-section references; and (iii) promote reliability by providing
self-assessments of both the derivation's soundness and the confidence of the
final answer. We apply ReFIne to the Qwen3 models at multiple scales
(1.7B/4B/8B) and evaluate across mathematical benchmarks of varying difficulty.
Our experimental results show that ReFIne models generate clearer and
better-structured reasoning traces (interpretability +44.0%), more faithfully
expose their underlying decision process (faithfulness +18.8%), and offer
informative confidence estimates (reliability +42.4%). These findings highlight
an overlooked but important direction: reasoning models should be optimized not
only for accuracy, but also for broader dimensions of trustworthiness. Our code
is available at:
https://github.com/Trustworthy-ML-Lab/Training_Trustworthy_LRM_with_Refine
Robot Learning: A Tutorial
Authors: name: Francesco Capuano, name: Caroline Pascal, name: Adil Zouitine, name: Thomas Wolf, name: Michel Aractingi
Published: 2025-10-14 11:36:46

Robot learning is at an inflection point, driven by rapid advancements in
machine learning and the growing availability of large-scale robotics data.
This shift from classical, model-based methods to data-driven, learning-based
paradigms is unlocking unprecedented capabilities in autonomous systems. This
tutorial navigates the landscape of modern robot learning, charting a course
from the foundational principles of Reinforcement Learning and Behavioral
Cloning to generalist, language-conditioned models capable of operating across
diverse tasks and even robot embodiments. This work is intended as a guide for
researchers and practitioners, and our goal is to equip the reader with the
conceptual understanding and practical tools necessary to contribute to
developments in robot learning, with ready-to-use examples implemented in
lerobot.