Orionnnn/hub-stats huggingface dataset:
CORRECT: COndensed eRror RECognition via knowledge Transfer in
  multi-agent systems
Authors: name: Yifan Yu, name: Moyan Li, name: Shaoyuan Xu, name: Jinmiao Fu, name: Xinhai Hou, name: Fan Lai, name: Bryan Wang
Published: 2025-09-28 21:47:20

Multi-agent systems (MAS) are increasingly capable of tackling complex
real-world tasks, yet their reliance on inter-agent coordination, tool use, and
long-horizon reasoning makes error recognition particularly challenging. Minor
errors can propagate across agents, escalating into task failures while
producing long, intertwined execution trajectories that impose significant
costs for both human developers and automated systems to debug and analyze. Our
key insight is that, despite surface differences in failure trajectories (e.g.,
logs), MAS errors often recur with similar structural patterns. This paper
presents CORRECT, the first lightweight, training-free framework that leverages
an online cache of distilled error schemata to recognize and transfer knowledge
of failure structures across new requests. This cache-based reuse allows LLMs
to perform targeted error localization at inference time, avoiding the need for
expensive retraining while adapting to dynamic MAS deployments in subseconds.
To support rigorous study in this domain, we also introduce CORRECT-Error, a
large-scale dataset of over 2,000 annotated trajectories collected through a
novel error-injection pipeline guided by real-world distributions, and further
validated through human evaluation to ensure alignment with natural failure
patterns. Experiments across seven diverse MAS applications show that CORRECT
improves step-level error localization up to 19.8% over existing advances while
at near-zero overhead, substantially narrowing the gap between automated and
human-level error recognition.
DeepCodeSeek: Real-Time API Retrieval for Context-Aware Code Generation
Authors: name: Esakkivel Esakkiraja, name: Denis Akhiyarov, name: Aditya Shanmugham, name: Chitra Ganapathy
Published: 2025-09-30 03:23:27

Current search techniques are limited to standard RAG query-document
applications. In this paper, we propose a novel technique to expand the code
and index for predicting the required APIs, directly enabling high-quality,
end-to-end code generation for auto-completion and agentic AI applications. We
address the problem of API leaks in current code-to-code benchmark datasets by
introducing a new dataset built from real-world ServiceNow Script Includes that
capture the challenge of unclear API usage intent in the code. Our evaluation
metrics show that this method achieves 87.86% top-40 retrieval accuracy,
allowing the critical context with APIs needed for successful downstream code
generation. To enable real-time predictions, we develop a comprehensive
post-training pipeline that optimizes a compact 0.6B reranker through synthetic
dataset generation, supervised fine-tuning, and reinforcement learning. This
approach enables our compact reranker to outperform a much larger 8B model
while maintaining 2.5x reduced latency, effectively addressing the nuances of
enterprise-specific code without the computational overhead of larger models.
Learning to Reason as Action Abstractions with Scalable Mid-Training RL
Authors: name: Shenao Zhang, name: Donghan Yu, name: Yihao Feng, name: Bowen Jin, name: Zhaoran Wang, name: John Peebles, name: Zirui Wang
Published: 2025-09-30 05:34:20

Large language models excel with reinforcement learning (RL), but fully
unlocking this potential requires a mid-training stage. An effective
mid-training phase should identify a compact set of useful actions and enable
fast selection among them through online RL. We formalize this intuition by
presenting the first theoretical result on how mid-training shapes
post-training: it characterizes an action subspace that minimizes both the
value approximation error from pruning and the RL error during subsequent
planning. Our analysis reveals two key determinants of mid-training
effectiveness: pruning efficiency, which shapes the prior of the initial RL
policy, and its impact on RL convergence, which governs the extent to which
that policy can be improved via online interactions. These results suggest that
mid-training is most effective when the decision space is compact and the
effective horizon is short, highlighting the importance of operating in the
space of action abstractions rather than primitive actions. Building on these
insights, we propose Reasoning as Action Abstractions (RA3), a scalable
mid-training algorithm. Specifically, we derive a sequential variational lower
bound and optimize it by iteratively discovering temporally-consistent latent
structures via RL, followed by fine-tuning on the bootstrapped data.
Experiments on code generation tasks demonstrate the effectiveness of our
approach. Across multiple base models, RA3 improves the average performance on
HumanEval and MBPP by 8 and 4 points over the base model and the next-token
prediction baseline. Furthermore, RA3 achieves faster convergence and higher
asymptotic performance in RLVR on HumanEval+, MBPP+, LiveCodeBench, and
Codeforces.
LLM Watermark Evasion via Bias Inversion
Authors: name: Jeongyeon Hwang, name: Sangdon Park, name: Jungseul Ok
Published: 2025-09-27 00:24:57

Watermarking for large language models (LLMs) embeds a statistical signal
during generation to enable detection of model-produced text. While
watermarking has proven effective in benign settings, its robustness under
adversarial evasion remains contested. To advance a rigorous understanding and
evaluation of such vulnerabilities, we propose the Bias-Inversion
Rewriting Attack (BIRA), which is theoretically motivated and model-agnostic.
BIRA weakens the watermark signal by suppressing the logits of likely
watermarked tokens during LLM-based rewriting, without any knowledge of the
underlying watermarking scheme. Across recent watermarking methods, BIRA
achieves over 99\% evasion while preserving the semantic content of the
original text. Beyond demonstrating an attack, our results reveal a systematic
vulnerability, emphasizing the need for stress testing and robust defenses.
Muon Outperforms Adam in Tail-End Associative Memory Learning
Authors: name: Shuche Wang, name: Fengzhuo Zhang, name: Jiaxiang Li, name: Cunxiao Du, name: Chao Du, name: Tianyu Pang, name: Zhuoran Yang, name: Mingyi Hong, name: Vincent Y. F. Tan
Published: 2025-09-30 10:04:08

The Muon optimizer is consistently faster than Adam in training Large
Language Models (LLMs), yet the mechanism underlying its success remains
unclear. This paper demystifies this mechanism through the lens of associative
memory. By ablating the transformer components optimized by Muon, we reveal
that the associative memory parameters of LLMs, namely the Value and Output
(VO) attention weights and Feed-Forward Networks (FFNs), are the primary
contributors to Muon's superiority. Motivated by this associative memory view,
we then explain Muon's superiority on real-world corpora, which are
intrinsically heavy-tailed: a few classes (tail classes) appear far less
frequently than others. The superiority is explained through two key
properties: (i) its update rule consistently yields a more isotropic singular
spectrum than Adam; and as a result, (ii) on heavy-tailed data, it optimizes
tail classes more effectively than Adam. Beyond empirical evidence, we
theoretically confirm these findings by analyzing a one-layer associative
memory model under class-imbalanced data. We prove that Muon consistently
achieves balanced learning across classes regardless of feature embeddings,
whereas Adam can induce large disparities in learning errors depending on
embedding properties. In summary, our empirical observations and theoretical
analyses reveal Muon's core advantage: its update rule aligns with the
outer-product structure of linear associative memories, enabling more balanced
and effective learning of tail classes in heavy-tailed distributions than Adam.
Nudging the Boundaries of LLM Reasoning
Authors: name: Justin Chih-Yao Chen, name: Becky Xiangyu Peng, name: Prafulla Kumar Choubey, name: Kung-Hsiang Huang, name: Jiaxin Zhang, name: Mohit Bansal, name: Chien-Sheng Wu
Published: 2025-09-30 02:01:40

Current online reinforcement learning (RL) algorithms like GRPO share a key
limitation in LLM reasoning: they cannot learn from problems that are
"unsolvable" to the model. In other words, they can only improve performance on
problems where the model is capable of exploring the correct answer.
Consequently, the model's "upper limit" remains unchanged after RL training,
even though the likelihood of solving easier, solvable problems may increase.
These hard samples cannot contribute to training, as no rollouts yield rewards
and thus no gradients are produced. To unlock learning from these hard samples,
we propose NuRL, a "nudging" method that aims to push the upper bound of LLM
reasoning using self-generated hints, i.e., abstract cues that help reduce the
problem difficulty for the model. Given a question and its gold answer, the
model generates a CoT and then produces a hint containing the core knowledge
needed to solve the problem. During training, we generate G rollouts from the
base policy and use the pass rate to decide whether the hint should be
injected. For hard samples with a 0% pass rate, we inject the hint and
regenerate a new batch of trajectories. This yields two benefits: (1) the hint
boosts pass rates (from 0% to non-zero), thereby introducing training signals
for previously unsolvable samples, and (2) the hints are self-generated,
avoiding distributional shift and do not rely on external models. NuRL achieves
consistent improvements across 6 benchmarks and 3 models, while remaining
complementary to test-time scaling. Notably, NuRL can raise the model's upper
limit, whereas GRPO leaves pass@1024 unchanged from the base model.
Furthermore, we present a systematic study of what makes an effective hint and
when hints are most useful. Interestingly, the best hints are abstract and
high-level, and are most beneficial when applied necessarily and after GRPO has
converged.
GeoRemover: Removing Objects and Their Causal Visual Artifacts
Authors: name: Zixin Zhu, name: Haoxiang Li, name: Xuelu Feng, name: He Wu, name: Chunming Qiao, name: Junsong Yuan
Published: 2025-09-23 02:04:19

Towards intelligent image editing, object removal should eliminate both the
target object and its causal visual artifacts, such as shadows and reflections.
However, existing image appearance-based methods either follow strictly
mask-aligned training and fail to remove these causal effects which are not
explicitly masked, or adopt loosely mask-aligned strategies that lack
controllability and may unintentionally over-erase other objects. We identify
that these limitations stem from ignoring the causal relationship between an
object's geometry presence and its visual effects. To address this limitation,
we propose a geometry-aware two-stage framework that decouples object removal
into (1) geometry removal and (2) appearance rendering. In the first stage, we
remove the object directly from the geometry (e.g., depth) using strictly
mask-aligned supervision, enabling structure-aware editing with strong
geometric constraints. In the second stage, we render a photorealistic RGB
image conditioned on the updated geometry, where causal visual effects are
considered implicitly as a result of the modified 3D geometry. To guide
learning in the geometry removal stage, we introduce a preference-driven
objective based on positive and negative sample pairs, encouraging the model to
remove objects as well as their causal visual artifacts while avoiding new
structural insertions. Extensive experiments demonstrate that our method
achieves state-of-the-art performance in removing both objects and their
associated artifacts on two popular benchmarks. The code is available at
https://github.com/buxiangzhiren/GeoRemover.
Thinking-Free Policy Initialization Makes Distilled Reasoning Models
  More Effective and Efficient Reasoners
Authors: name: Xin Xu, name: Cliveb AI, name: Kai Yang, name: Tianhao Chen, name: Yang Wang, name: Saiyong Yang, name: Can Yang
Published: 2025-09-30 13:25:00

Reinforcement Learning with Verifiable Reward (RLVR) effectively solves
complex tasks but demands extremely long context lengths during training,
leading to substantial computational costs. While multi-stage training can
partially mitigate this, starting with overly short contexts often causes
irreversible performance degradation, ultimately failing to reduce overall
training compute significantly. In this paper, we introduce
**T**hinking-**F**ree **P**olicy **I**nitialization (**TFPI**), a simple yet
effective adaptation to RLVR that bridges long Chain-of-Thought (CoT)
distillation and standard RLVR. TFPI employs a simple *ThinkFree* operation,
explicitly discarding the thinking content via a direct *</think>* append, to
reduce token usage during inference. Training with *ThinkFree*-adapted inputs
improves performance and lowers token consumption, even in the original
slow-thinking mode. Extensive experiments across various benchmarks have shown
that TFPI accelerates RL convergence, achieves a higher performance ceiling,
and yields more token-efficient reasoning models without specialized rewards or
complex training designs. With TFPI only, we train a 4B model to reach 89.0%
accuracy on AIME24 and 65.5% on LiveCodeBench using less than 4K H20 hours.
Convolutional Set Transformer
Authors: name: Federico Chinello, name: Giacomo Boracchi
Published: 2025-09-26 20:13:00

We introduce the Convolutional Set Transformer (CST), a novel neural
architecture designed to process image sets of arbitrary cardinality that are
visually heterogeneous yet share high-level semantics - such as a common
category, scene, or concept. Existing set-input networks, e.g., Deep Sets and
Set Transformer, are limited to vector inputs and cannot directly handle 3D
image tensors. As a result, they must be cascaded with a feature extractor,
typically a CNN, which encodes images into embeddings before the set-input
network can model inter-image relationships. In contrast, CST operates directly
on 3D image tensors, performing feature extraction and contextual modeling
simultaneously, thereby enabling synergies between the two processes. This
design yields superior performance in tasks such as Set Classification and Set
Anomaly Detection and further provides native compatibility with CNN
explainability methods such as Grad-CAM, unlike competing approaches that
remain opaque. Finally, we show that CSTs can be pre-trained on large-scale
datasets and subsequently adapted to new domains and tasks through standard
Transfer Learning schemes. To support further research, we release CST-15, a
CST backbone pre-trained on ImageNet
(https://github.com/chinefed/convolutional-set-transformer).
Video Object Segmentation-Aware Audio Generation
Authors: name: Ilpo Viertola, name: Vladimir Iashin, name: Esa Rahtu
Published: 2025-09-30 17:49:41

Existing multimodal audio generation models often lack precise user control,
which limits their applicability in professional Foley workflows. In
particular, these models focus on the entire video and do not provide precise
methods for prioritizing a specific object within a scene, generating
unnecessary background sounds, or focusing on the wrong objects. To address
this gap, we introduce the novel task of video object segmentation-aware audio
generation, which explicitly conditions sound synthesis on object-level
segmentation maps. We present SAGANet, a new multimodal generative model that
enables controllable audio generation by leveraging visual segmentation masks
along with video and textual cues. Our model provides users with fine-grained
and visually localized control over audio generation. To support this task and
further research on segmentation-aware Foley, we propose Segmented Music Solos,
a benchmark dataset of musical instrument performance videos with segmentation
information. Our method demonstrates substantial improvements over current
state-of-the-art methods and sets a new standard for controllable,
high-fidelity Foley synthesis. Code, samples, and Segmented Music Solos are
available at https://saganet.notion.site
Context Is What You Need: The Maximum Effective Context Window for Real
  World Limits of LLMs
Authors: name: Norman Paulsen
Published: 2025-09-21 14:38:17

Large language model (LLM) providers boast big numbers for maximum context
window sizes. To test the real world use of context windows, we 1) define a
concept of maximum effective context window, 2) formulate a testing method of a
context window's effectiveness over various sizes and problem types, and 3)
create a standardized way to compare model efficacy for increasingly larger
context window sizes to find the point of failure. We collected hundreds of
thousands of data points across several models and found significant
differences between reported Maximum Context Window (MCW) size and Maximum
Effective Context Window (MECW) size. Our findings show that the MECW is, not
only, drastically different from the MCW but also shifts based on the problem
type. A few top of the line models in our test group failed with as little as
100 tokens in context; most had severe degradation in accuracy by 1000 tokens
in context. All models fell far short of their Maximum Context Window by as
much as 99 percent. Our data reveals the Maximum Effective Context Window
shifts based on the type of problem provided, offering clear and actionable
insights into how to improve model accuracy and decrease model hallucination
rates.
Specialization after Generalization: Towards Understanding Test-Time
  Training in Foundation Models
Authors: name: Jonas Hübotter, name: Patrik Wolf, name: Alexander Shevchenko, name: Dennis Jüni, name: Andreas Krause, name: Gil Kur
Published: 2025-09-29 09:24:52

Recent empirical studies have explored the idea of continuing to train a
model at test-time for a given task, known as test-time training (TTT), and
have found it to yield significant performance improvements. However, there is
limited understanding of why and when TTT is effective. Earlier explanations
mostly focused on the observation that TTT may help when applied to
out-of-distribution adaptation or used with privileged data. However, the
growing scale of foundation models with most test data being in-distribution
questions these explanations. We instead posit that foundation models remain
globally underparameterized, with TTT providing a mechanism for specialization
after generalization, focusing capacity on concepts relevant to the test task.
Specifically, under the linear representation hypothesis, we propose a model in
which TTT achieves a substantially smaller in-distribution test error than
global training. We empirically validate our model's key assumptions by
training a sparse autoencoder on ImageNet, showing that semantically related
data points are explained by only a few shared concepts. Finally, we perform
scaling studies across image and language tasks that confirm the practical
implications of our model, identifying the regimes where specialization is most
effective.
The Dragon Hatchling: The Missing Link between the Transformer and
  Models of the Brain
Authors: name: Adrian Kosowski, name: Przemysław Uznański, name: Jan Chorowski, name: Zuzanna Stamirowska, name: Michał Bartoszkiewicz
Published: 2025-09-30 16:49:01

The relationship between computing systems and the brain has served as
motivation for pioneering theoreticians since John von Neumann and Alan Turing.
Uniform, scale-free biological networks, such as the brain, have powerful
properties, including generalizing over time, which is the main barrier for
Machine Learning on the path to Universal Reasoning Models.
  We introduce `Dragon Hatchling' (BDH), a new Large Language Model
architecture based on a scale-free biologically inspired network of \n
locally-interacting neuron particles. BDH couples strong theoretical
foundations and inherent interpretability without sacrificing Transformer-like
performance.
  BDH is a practical, performant state-of-the-art attention-based state space
sequence learning architecture. In addition to being a graph model, BDH admits
a GPU-friendly formulation. It exhibits Transformer-like scaling laws:
empirically BDH rivals GPT2 performance on language and translation tasks, at
the same number of parameters (10M to 1B), for the same training data.
  BDH can be represented as a brain model. The working memory of BDH during
inference entirely relies on synaptic plasticity with Hebbian learning using
spiking neurons. We confirm empirically that specific, individual synapses
strengthen connection whenever BDH hears or reasons about a specific concept
while processing language inputs. The neuron interaction network of BDH is a
graph of high modularity with heavy-tailed degree distribution. The BDH model
is biologically plausible, explaining one possible mechanism which human
neurons could use to achieve speech.
  BDH is designed for interpretability. Activation vectors of BDH are sparse
and positive. We demonstrate monosemanticity in BDH on language tasks.
Interpretability of state, which goes beyond interpretability of neurons and
model parameters, is an inherent feature of the BDH architecture.
d^2Cache: Accelerating Diffusion-Based LLMs via Dual Adaptive Caching
Authors: name: Yuchu Jiang, name: Yue Cai, name: Xiangzhong Luo, name: Jiale Fu, name: Jiarui Wang, name: Chonghan Liu, name: Xu Yang
Published: 2025-09-27 04:07:23

Diffusion-based large language models (dLLMs), despite their promising
performance, still suffer from inferior inference efficiency. This is because
dLLMs rely on bidirectional attention and cannot directly benefit from the
standard key-value (KV) cache as autoregressive models (ARMs) do. To tackle
this issue, we introduce Dual aDaptive Cache (d^2Cache), which is a
training-free approximate KV cache framework for accelerating dLLM inference.
d^2Cache features a two-stage fine-grained selection strategy to identify
tokens and adaptively update their KV states at each decoding step, while
caching the KV states of the remaining tokens for reuse. Furthermore,
d^2Cache naturally offers a more reliable decoding alternative, which can
enable quasi left-to-right generation and mitigate premature overconfidence in
tokens at the end of the sequence. Extensive experimental results on two
representative dLLMs (\ie, LLaDA and Dream) demonstrate that d^2Cache not
only achieves substantial inference speedups, but also yields consistent
improvements in generation quality. The code is available at
https://github.com/Kamichanw/d2Cache.