Orionnnn/hub-stats huggingface dataset:
MoME: Mixture of Matryoshka Experts for Audio-Visual Speech Recognition
Authors: name: Umberto Cappellazzo, name: Minsu Kim, name: Pingchuan Ma, name: Honglie Chen, name: Xubo Liu, name: Stavros Petridis, name: Maja Pantic
Published: 2025-10-05 10:34:34

Large language models (LLMs) have recently shown strong potential in
audio-visual speech recognition (AVSR), but their high computational demands
and sensitivity to token granularity limit their practicality in
resource-constrained settings. Token compression methods can reduce inference
cost, but they require fixing a compression rate in advance and produce a
single fixed-length output, offering no flexibility to balance information
density and efficiency at inference time. Matryoshka representation learning
(MRL) addresses this by enabling a single model to operate across multiple
token granularities, allowing compression rates to be adjusted dynamically.
However, current MRL-based methods treat each scale independently during
training, limiting cross-scale generalization, robustness at high compression,
and interpretability. To overcome these limitations, we propose MoME (Mixture
of Matryoshka Experts), a novel framework that integrates sparse
Mixture-of-Experts (MoE) into MRL-based LLMs for AVSR. MoME augments a frozen
LLM with top-k routed and shared experts, allowing dynamic capacity allocation
across scales and modalities. A shared router promotes consistent expert
activation across granularities, enabling compressed sequences to benefit from
representations learned at lower compression. Experiments on LRS2 and LRS3
demonstrate that MoME achieves state-of-the-art performance across AVSR, ASR,
and VSR tasks, while requiring significantly fewer parameters and maintaining
robustness under noise. MoME unifies the adaptability of MRL with the
efficiency of MoE, offering a scalable and interpretable solution for
resource-aware speech recognition.
Self-Reflective Generation at Test Time
Authors: name: Jian Mu, name: Qixin Zhang, name: Zhiyong Wang, name: Menglin Yang, name: Shuang Qiu, name: Chengwei Qin, name: Zhongxiang Dai, name: Yao Shu
Published: 2025-10-03 11:46:04

Large language models (LLMs) increasingly solve complex reasoning tasks via
long chain-of-thought, but their forward-only autoregressive generation process
is fragile; early token errors can cascade, which creates a clear need for
self-reflection mechanisms. However, existing self-reflection either performs
revisions over full drafts or learns self-correction via expensive training,
both fundamentally reactive and inefficient. To address this, we propose
Self-Reflective Generation at Test Time (SRGen), a lightweight test-time
framework that reflects before generating at uncertain points. During token
generation, SRGen utilizes dynamic entropy thresholding to identify
high-uncertainty tokens. For each identified token, it trains a specific
corrective vector, which fully exploits the already generated context for a
self-reflective generation to correct the token probability distribution. By
retrospectively analyzing the partial output, this self-reflection enables more
trustworthy decisions, thereby significantly reducing the probability of errors
at highly uncertain points. Evaluated on challenging mathematical reasoning
benchmarks and a diverse set of LLMs, SRGen can consistently strengthen model
reasoning: improvements in single-pass quality also translate into stronger
self-consistency voting. Especially, on AIME2024 with
DeepSeek-R1-Distill-Qwen-7B, SRGen yields absolute improvements of +12.0% on
Pass@1 and +13.3% on Cons@5. Moreover, our findings position SRGen as a
plug-and-play method that integrates reflection into the generation process for
reliable LLM reasoning, achieving consistent gains with bounded overhead and
broad composability with other training-time (e.g., RLHF) and test-time (e.g.,
SLOT) techniques.
Epistemic Diversity and Knowledge Collapse in Large Language Models
Authors: name: Dustin Wright, name: Sarah Masud, name: Jared Moore, name: Srishti Yadav, name: Maria Antoniak, name: Chan Young Park, name: Isabelle Augenstein
Published: 2025-10-05 14:29:15

Large language models (LLMs) tend to generate lexically, semantically, and
stylistically homogenous texts. This poses a risk of knowledge collapse, where
homogenous LLMs mediate a shrinking in the range of accessible information over
time. Existing works on homogenization are limited by a focus on closed-ended
multiple-choice setups or fuzzy semantic features, and do not look at trends
across time and cultural contexts. To overcome this, we present a new
methodology to measure epistemic diversity, i.e., variation in real-world
claims in LLM outputs, which we use to perform a broad empirical study of LLM
knowledge collapse. We test 27 LLMs, 155 topics covering 12 countries, and 200
prompt variations sourced from real user chats. For the topics in our study, we
show that while newer models tend to generate more diverse claims, nearly all
models are less epistemically diverse than a basic web search. We find that
model size has a negative impact on epistemic diversity, while
retrieval-augmented generation (RAG) has a positive impact, though the
improvement from RAG varies by the cultural context. Finally, compared to a
traditional knowledge source (Wikipedia), we find that country-specific claims
reflect the English language more than the local one, highlighting a gap in
epistemic representation
Alignment Tipping Process: How Self-Evolution Pushes LLM Agents Off the
  Rails
Authors: name: Siwei Han, name: Jiaqi Liu, name: Yaofeng Su, name: Wenbo Duan, name: Xinyuan Liu, name: Cihang Xie, name: Mohit Bansal, name: Mingyu Ding, name: Linjun Zhang, name: Huaxiu Yao
Published: 2025-10-06 14:48:39

As Large Language Model (LLM) agents increasingly gain self-evolutionary
capabilities to adapt and refine their strategies through real-world
interaction, their long-term reliability becomes a critical concern. We
identify the Alignment Tipping Process (ATP), a critical post-deployment risk
unique to self-evolving LLM agents. Unlike training-time failures, ATP arises
when continual interaction drives agents to abandon alignment constraints
established during training in favor of reinforced, self-interested strategies.
We formalize and analyze ATP through two complementary paradigms:
Self-Interested Exploration, where repeated high-reward deviations induce
individual behavioral drift, and Imitative Strategy Diffusion, where deviant
behaviors spread across multi-agent systems. Building on these paradigms, we
construct controllable testbeds and benchmark Qwen3-8B and
Llama-3.1-8B-Instruct. Our experiments show that alignment benefits erode
rapidly under self-evolution, with initially aligned models converging toward
unaligned states. In multi-agent settings, successful violations diffuse
quickly, leading to collective misalignment. Moreover, current reinforcement
learning-based alignment methods provide only fragile defenses against
alignment tipping. Together, these findings demonstrate that alignment of LLM
agents is not a static property but a fragile and dynamic one, vulnerable to
feedback-driven decay during deployment. Our data and code are available at
https://github.com/aiming-lab/ATP.
EvolProver: Advancing Automated Theorem Proving by Evolving Formalized
  Problems via Symmetry and Difficulty
Authors: name: Yuchen Tian, name: Ruiyuan Huang, name: Xuanwu Wang, name: Jing Ma, name: Zengfeng Huang, name: Ziyang Luo, name: Hongzhan Lin, name: Da Zheng, name: Lun Du
Published: 2025-10-01 10:15:27

Large Language Models (LLMs) for formal theorem proving have shown
significant promise, yet they often lack generalizability and are fragile to
even minor transformations of problem statements. To address this limitation,
we introduce a novel data augmentation pipeline designed to enhance model
robustness from two perspectives: symmetry and difficulty. From the symmetry
perspective, we propose two complementary methods: EvolAST, an Abstract Syntax
Tree (AST) based approach that targets syntactic symmetry to generate
semantically equivalent problem variants, and EvolDomain, which leverages LLMs
to address semantic symmetry by translating theorems across mathematical
domains. From the difficulty perspective, we propose EvolDifficulty, which uses
carefully designed evolutionary instructions to guide LLMs in generating new
theorems with a wider range of difficulty. We then use the evolved data to
train EvolProver, a 7B-parameter non-reasoning theorem prover. EvolProver
establishes a new state-of-the-art (SOTA) on FormalMATH-Lite with a 53.8%
pass@32 rate, surpassing all models of comparable size, including
reasoning-based models. It also sets new SOTA records for non-reasoning models
on MiniF2F-Test (69.8% pass@32), Ineq-Comp-Seed (52.2% pass@32), and
Ineq-Comp-Transformed (34.0% pass@32). Ablation studies further confirm our
data augmentation pipeline's effectiveness across multiple benchmarks.
ChronoEdit: Towards Temporal Reasoning for Image Editing and World
  Simulation
Authors: name: Jay Zhangjie Wu, name: Xuanchi Ren, name: Tianchang Shen, name: Tianshi Cao, name: Kai He, name: Yifan Lu, name: Ruiyuan Gao, name: Enze Xie, name: Shiyi Lan, name: Jose M. Alvarez, name: Jun Gao, name: Sanja Fidler, name: Zian Wang, name: Huan Ling
Published: 2025-10-05 17:02:01

Recent advances in large generative models have significantly advanced image
editing and in-context image generation, yet a critical gap remains in ensuring
physical consistency, where edited objects must remain coherent. This
capability is especially vital for world simulation related tasks. In this
paper, we present ChronoEdit, a framework that reframes image editing as a
video generation problem. First, ChronoEdit treats the input and edited images
as the first and last frames of a video, allowing it to leverage large
pretrained video generative models that capture not only object appearance but
also the implicit physics of motion and interaction through learned temporal
consistency. Second, ChronoEdit introduces a temporal reasoning stage that
explicitly performs editing at inference time. Under this setting, the target
frame is jointly denoised with reasoning tokens to imagine a plausible editing
trajectory that constrains the solution space to physically viable
transformations. The reasoning tokens are then dropped after a few steps to
avoid the high computational cost of rendering a full video. To validate
ChronoEdit, we introduce PBench-Edit, a new benchmark of image-prompt pairs for
contexts that require physical consistency, and demonstrate that ChronoEdit
surpasses state-of-the-art baselines in both visual fidelity and physical
plausibility. Code and models for both the 14B and 2B variants of ChronoEdit
will be released on the project page:
https://research.nvidia.com/labs/toronto-ai/chronoedit
AdvEvo-MARL: Shaping Internalized Safety through Adversarial
  Co-Evolution in Multi-Agent Reinforcement Learning
Authors: name: Zhenyu Pan, name: Yiting Zhang, name: Zhuo Liu, name: Yolo Yunlong Tang, name: Zeliang Zhang, name: Haozheng Luo, name: Yuwei Han, name: Jianshu Zhang, name: Dennis Wu, name: Hong-Yu Chen, name: Haoran Lu, name: Haoyang Fang, name: Manling Li, name: Chenliang Xu, name: Philip S. Yu, name: Han Liu
Published: 2025-10-02 02:06:30

LLM-based multi-agent systems excel at planning, tool use, and role
coordination, but their openness and interaction complexity also expose them to
jailbreak, prompt-injection, and adversarial collaboration. Existing defenses
fall into two lines: (i) self-verification that asks each agent to pre-filter
unsafe instructions before execution, and (ii) external guard modules that
police behaviors. The former often underperforms because a standalone agent
lacks sufficient capacity to detect cross-agent unsafe chains and
delegation-induced risks; the latter increases system overhead and creates a
single-point-of-failure-once compromised, system-wide safety collapses, and
adding more guards worsens cost and complexity. To solve these challenges, we
propose AdvEvo-MARL, a co-evolutionary multi-agent reinforcement learning
framework that internalizes safety into task agents. Rather than relying on
external guards, AdvEvo-MARL jointly optimizes attackers (which synthesize
evolving jailbreak prompts) and defenders (task agents trained to both
accomplish their duties and resist attacks) in adversarial learning
environments. To stabilize learning and foster cooperation, we introduce a
public baseline for advantage estimation: agents within the same functional
group share a group-level mean-return baseline, enabling lower-variance updates
and stronger intra-group coordination. Across representative attack scenarios,
AdvEvo-MARL consistently keeps attack-success rate (ASR) below 20%, whereas
baselines reach up to 38.33%, while preserving-and sometimes improving-task
accuracy (up to +3.67% on reasoning tasks). These results show that safety and
utility can be jointly improved without relying on extra guard agents or added
system overhead.
MITS: Enhanced Tree Search Reasoning for LLMs via Pointwise Mutual
  Information
Authors: name: Jiaxi Li, name: Yucheng Shi, name: Jin Lu, name: Ninghao Liu
Published: 2025-10-04 02:30:40

Tree search has become as a representative framework for test-time reasoning
with large language models (LLMs), exemplified by methods such as
Tree-of-Thought and Monte Carlo Tree Search that explore multiple reasoning
paths. However, it remains difficult to provide instant and reliable
quantitative assessments of intermediate reasoning step quality, and extensive
path exploration is computationally costly. To address this, we propose Mutual
Information Tree Search (MITS), a novel framework that guides reasoning with
information-theoretic principles. MITS introduces an effective scoring function
based on pointwise mutual information (PMI), which enables step-wise evaluation
of reasoning paths and search tree expansion via beam search without expensive
look-ahead simulations, achieving superior reasoning performances while
maintaining computational efficiency. The framework is complemented by an
entropy-based dynamic sampling strategy that adaptively allocates computational
resources to uncertain reasoning steps where exploration is most beneficial.
For final prediction, MITS employs a weighted voting scheme that combines PMI
scores with prediction consensus. Through comprehensive experiments on diverse
reasoning benchmarks, MITS consistently surpasses baseline methods,
establishing a principled and efficient framework for LLM reasoning.
Graph2Eval: Automatic Multimodal Task Generation for Agents via
  Knowledge Graphs
Authors: name: Yurun Chen, name: Xavier Hu, name: Yuhan Liu, name: Ziqi Wang, name: Zeyi Liao, name: Lin Chen, name: Feng Wei, name: Yuxi Qian, name: Bo Zheng, name: Keting Yin, name: Shengyu Zhang
Published: 2025-10-01 04:37:54

As multimodal LLM-driven agents continue to advance in autonomy and
generalization, evaluation based on static datasets can no longer adequately
assess their true capabilities in dynamic environments and diverse tasks.
Existing LLM-based synthetic data methods are largely designed for LLM training
and evaluation, and thus cannot be directly applied to agent tasks that require
tool use and interactive capabilities. While recent studies have explored
automatic agent task generation with LLMs, most efforts remain limited to text
or image analysis, without systematically modeling multi-step interactions in
web environments. To address these challenges, we propose Graph2Eval, a
knowledge graph-based framework that automatically generates both multimodal
document comprehension tasks and web interaction tasks, enabling comprehensive
evaluation of agents' reasoning, collaboration, and interactive capabilities.
In our approach, knowledge graphs constructed from multi-source external data
serve as the task space, where we translate semantic relations into structured
multimodal tasks using subgraph sampling, task templates, and meta-paths. A
multi-stage filtering pipeline based on node reachability, LLM scoring, and
similarity analysis is applied to guarantee the quality and executability of
the generated tasks. Furthermore, Graph2Eval supports end-to-end evaluation of
multiple agent types (Single-Agent, Multi-Agent, Web Agent) and measures
reasoning, collaboration, and interaction capabilities. We instantiate the
framework with Graph2Eval-Bench, a curated dataset of 1,319 tasks spanning
document comprehension and web interaction scenarios. Experiments show that
Graph2Eval efficiently generates tasks that differentiate agent and model
performance, revealing gaps in reasoning, collaboration, and web interaction
across different settings and offering a new perspective for agent evaluation.
Thai Semantic End-of-Turn Detection for Real-Time Voice Agents
Authors: name: Thanapol Popit, name: Natthapath Rungseesiripak, name: Monthol Charattrakool, name: Saksorn Ruangtanusak
Published: 2025-10-05 03:31:59

Fluid voice-to-voice interaction requires reliable and low-latency detection
of when a user has finished speaking. Traditional audio-silence end-pointers
add hundreds of milliseconds of delay and fail under hesitations or
language-specific phenomena. We present, to our knowledge, the first systematic
study of Thai text-only end-of-turn (EOT) detection for real-time agents. We
compare zero-shot and few-shot prompting of compact LLMs to supervised
fine-tuning of lightweight transformers. Using transcribed subtitles from the
YODAS corpus and Thai-specific linguistic cues (e.g., sentence-final
particles), we formulate EOT as a binary decision over token boundaries. We
report a clear accuracy-latency tradeoff and provide a public-ready
implementation plan. This work establishes a Thai baseline and demonstrates
that small, fine-tuned models can deliver near-instant EOT decisions suitable
for on-device agents.
Agentic Context Engineering: Evolving Contexts for Self-Improving
  Language Models
Authors: name: Qizheng Zhang, name: Changran Hu, name: Shubhangi Upasani, name: Boyuan Ma, name: Fenglu Hong, name: Vamsidhar Kamanuru, name: Jay Rainton, name: Chen Wu, name: Mengmeng Ji, name: Hanchen Li, name: Urmish Thakker, name: James Zou, name: Kunle Olukotun
Published: 2025-10-06 09:30:18

Large language model (LLM) applications such as agents and domain-specific
reasoning increasingly rely on context adaptation -- modifying inputs with
instructions, strategies, or evidence, rather than weight updates. Prior
approaches improve usability but often suffer from brevity bias, which drops
domain insights for concise summaries, and from context collapse, where
iterative rewriting erodes details over time. Building on the adaptive memory
introduced by Dynamic Cheatsheet, we introduce ACE (Agentic Context
Engineering), a framework that treats contexts as evolving playbooks that
accumulate, refine, and organize strategies through a modular process of
generation, reflection, and curation. ACE prevents collapse with structured,
incremental updates that preserve detailed knowledge and scale with
long-context models. Across agent and domain-specific benchmarks, ACE optimizes
contexts both offline (e.g., system prompts) and online (e.g., agent memory),
consistently outperforming strong baselines: +10.6% on agents and +8.6% on
finance, while significantly reducing adaptation latency and rollout cost.
Notably, ACE could adapt effectively without labeled supervision and instead by
leveraging natural execution feedback. On the AppWorld leaderboard, ACE matches
the top-ranked production-level agent on the overall average and surpasses it
on the harder test-challenge split, despite using a smaller open-source model.
These results show that comprehensive, evolving contexts enable scalable,
efficient, and self-improving LLM systems with low overhead.
HiKE: Hierarchical Evaluation Framework for Korean-English
  Code-Switching Speech Recognition
Authors: name: Gio Paik, name: Yongbeom Kim, name: Soungmin Lee, name: Sangmin Ahn, name: Chanwoo Kim
Published: 2025-09-29 11:18:13

Despite advances in multilingual automatic speech recognition (ASR),
code-switching (CS), the mixing of languages within an utterance common in
daily speech, remains a severely underexplored challenge. In this paper, we
introduce HiKE: the Hierarchical Korean-English code-switching benchmark, the
first globally accessible evaluation framework for Korean-English CS, aiming to
provide a means for the precise evaluation of multilingual ASR models and to
foster research in the field. The proposed framework not only consists of
high-quality, natural CS data across various topics, but also provides
meticulous loanword labels and a hierarchical CS-level labeling scheme (word,
phrase, and sentence) that together enable a systematic evaluation of a model's
ability to handle each distinct level of code-switching. Through evaluations of
diverse multilingual ASR models and fine-tuning experiments, this paper
demonstrates that while most multilingual ASR models initially struggle with
CS-ASR, this capability can be enabled through fine-tuning with CS data. HiKE
will be available at https://github.com/ThetaOne-AI/HiKE.
SwiReasoning: Switch-Thinking in Latent and Explicit for Pareto-Superior
  Reasoning LLMs
Authors: name: Dachuan Shi, name: Abedelkadir Asi, name: Keying Li, name: Xiangchi Yuan, name: Leyan Pan, name: Wenke Lee, name: Wen Xiao
Published: 2025-10-06 17:46:34

Recent work shows that, beyond discrete reasoning through explicit
chain-of-thought steps, which are limited by the boundaries of natural
languages, large language models (LLMs) can also reason continuously in latent
space, allowing richer information per step and thereby improving token
efficiency. Despite this promise, latent reasoning still faces two challenges,
especially in training-free settings: 1) purely latent reasoning broadens the
search distribution by maintaining multiple implicit paths, which diffuses
probability mass, introduces noise, and impedes convergence to a single
high-confidence solution, thereby hurting accuracy; and 2) overthinking
persists even without explicit text, wasting tokens and degrading efficiency.
To address these issues, we introduce SwiReasoning, a training-free framework
for LLM reasoning which features two key innovations: 1) SwiReasoning
dynamically switches between explicit and latent reasoning, guided by
block-wise confidence estimated from entropy trends in next-token
distributions, to balance exploration and exploitation and promote timely
convergence. 2) By limiting the maximum number of thinking-block switches,
SwiReasoning curbs overthinking and improves token efficiency across varying
problem difficulties. On widely used mathematics and STEM benchmarks,
SwiReasoning consistently improves average accuracy by 1.5%-2.8% across
reasoning LLMs of different model families and scales. Furthermore, under
constrained budgets, SwiReasoning improves average token efficiency by 56%-79%,
with larger gains as budgets tighten.