论文检索

Deep Learning · Attention Mechanisms

Antonio Álvarez López, Borjan Geshkovski, Domènec Ruiz-Balet

The forward pass of a Transformer can be seen as an interacting particle system on the unit sphere: time plays the role of layers, particles that of token embeddings, and the unit sphere idealizes layer normalization. In some weight settings the system can even be seen as a gradient flow for an explicit energy, and one can make sense of the infinite context length (\emph{mean-field}) limit thanks to Wasserstein gradient flows. In this paper we study the effect of the perceptron block in this setting, and show that critical points are generically atomic and localized on subsets of the sphere.

Applications · Everything Else

Ayano Hiranaka, Ya-Chuan Hsu, Stefanos Nikolaidis, Erdem Biyik, Daniel Seita

AI assistants in human-AI collaboration often correct suboptimal human actions through behavioral feedback (e.g., alerts or steering-wheel nudges in assistive driving). Such interventions can mitigate immediate errors, but long-term improvement requires addressing the underlying misconceptions that cause repeated mistakes. We introduce SENSEI, a framework that infers user misconceptions from interaction behavior and provides targeted, minimal yet sufficient suggestions to correct them. Our approach departs from action- or trajectory-level interventions by operating over a structured knowledge representation to localize and correct the sources of erroneous behavior. Across three long-horizon tasks with diverse misconceptions and corresponding behaviors, SENSEI demonstrates zero-shot compositional generalization, disentangling multiple overlapping misconceptions despite training only on single-misconception cases. A user study further shows that our method identifies real human misconceptions and provides effective guidance that improves long-horizon task performance, successfully correcting 90% of student misconceptions.

Deep Learning · Large Language Models

Zhenda Xie, Yixuan Wei, Huanqi Cao, Chenggang Zhao, Chengqi Deng, Jiashi Li, Damai Dai, Huazuo Gao, Mingyu Xu, Kuai Yu 等

Recently, studies exemplified by Hyper-Connections (HC) have extended the ubiquitous residual connection paradigm established over the past decade by expanding the residual stream width and diversifying connectivity patterns. While yielding substantial performance gains, this diversification fundamentally compromises the identity mapping property intrinsic to the residual connection, which causes severe training instability and restricted scalability, and additionally incurs notable memory access overhead. To address these challenges, we propose Manifold-Constrained Hyper-Connections (mHC), a general framework that projects the residual connection space of HC onto a specific manifold to restore the identity mapping property, while incorporating rigorous infrastructure optimization to ensure efficiency. Empirical experiments demonstrate that mHC is effective for training at scale, offering tangible performance improvements and superior scalability. We anticipate that mHC, as a flexible and practical extension of HC, will contribute to a deeper understanding of topological architecture design and suggest promising directions for the evolution of foundational models.

Applications · Time Series

Jiaen Lv, Leran Qi, Shaowei Wang

Time series prototype learning is fundamentally challenged by observational ambiguity. Discrete architectures fail to resolve this, as they lack the capacity to decouple stochastic noise from continuous dynamics. Furthermore, rigid closed-set assumptions fail to capture unseen diversity. To address these limitations, we propose a hierarchical ordinary differential equation clustering network, which utilizes neural ordinary differential equation to model latent state evolution as a continuous integral curve. This formulation enforces temporal continuity to effectively disentangle smooth feature trends from stochastic noise, while our adaptive hierarchical mechanism autonomously identifies the optimal number of prototypes without rigid prior constraints. Validated on the early link failure detection task with irregularly sampled time series, the proposed method effectively extracts underlying physical prototypes, thereby enabling robust failure detection.

General Machine Learning · Transfer, Multitask and Meta-learning

Adam Ousherovitch, Yixin Wang

Modern learning systems excel at interpolation but struggle to generalize to unseen tasks outside the training distribution's support. This failure occurs even in simple settings, such as handling task parameters beyond the training range, and persists despite advances in foundation models. To this end, we develop the Relational Task Extrapolator (RTE), an algorithm designed to enable systematic extrapolation to novel tasks. The key observation is that extrapolation is inherently relational: extrapolating to unseen tasks requires learning how tasks transform into one another. If a model learns the transformation between tasks A and B during training, it can apply that same transformation to relate known tasks to unseen ones at test time. RTE operationalizes this idea by decomposing each target task into a known anchor task and a transformation linking the anchor and target. It then learns a relational operator, mapping an anchor–transformation pair to predictions for the target task. We instantiate RTE across multiple task extrapolation regimes in function prediction, e.g. where target tasks use out-of-range parameters (parameter extrapolation), has greater compositional depth (length extrapolation), and/or recombine function primitives in unseen ways (compositional extrapolation). We further extend RTE to sequence prediction, integrating it into fine-tuning algorithms for foundation models. Across empirical studies, we find that RTE substantially outperforms existing approaches on extrapolation to novel, unseen tasks.

Applications · Everything Else

Yiheng Hu, Xiaoyang Wang, Qing Liu, Sherry Xu, Qian Fu, Wenjie Zhang

Knowledge-based Visual Question Answering (KB-VQA) remains a challenging task, particularly when queries require precise identification and grounding of fine-grained entities within large-scale knowledge base. Existing methods often treat visual and textual signals in isolation and rely heavily on image-centric retrieval, which makes them sensitive to visual ambiguities. To address these limitations, we propose EntRAG, an entity-centric retrieval-augmented generation framework. Our approach first introduces EntBind to align query representations with multimodal entity embeddings by explicitly binding entity tokens to latent visual features, retrieving a set of relevant candidate entities. A reranking mechanism is applied to these candidate entities to select the most informative context by combining entity-level alignment with overall contextual relevance. The selected evidence is incorporated into context-aware generation module to produce final answer. By explicitly operating at the entity level, EntRAG achieves more consistent and reliable results. Extensive experiments demonstrate that EntRAG consistently outperforms prior methods, achieving scores of 45.2 on E-VQA and 43.8 on InfoSeek.

Social Aspects · Accountability, Transparency, and Interpretability

Hak Hyun Kim, Benjamin Huh, Soroush Vosoughi

Multi-Agent Systems (MAS) are deployed at unprecedented scale—from warehouse robot fleets to autonomous vehicle networks to collaborative LLM agents—yet methods for explaining their behavior remain fragmented and underspecified. We analyze 2,381 MAS-related papers from top machine learning venues (2021–2025) and find systematic gaps: 65% omit stakeholder specifications, 76% lack quantitative evaluation bounds, and 99% ignore auditability requirements. These gaps render current MAS XAI research non-comparable, non-reproducible, and disconnected from deployment requirements. We argue that MAS XAI research requires explicit specification of two contracts before developing methods. The **Research Contract** defines six elements: explanandum, stakeholder, intervention unit, evaluation bounds, adversarial context, auditability. The **Agent Contract** defines expected behaviors through obligations, permissions, prohibitions, violation criteria, and accountability chains—providing the baseline against which deviations are explained. These contracts are method-agnostic and architecture-agnostic, applicable to LLM-based, learning-based, and hybrid MAS. Through case studies spanning warehouse robotics, autonomous vehicles, and LLM agent systems, we demonstrate that contracts transform vague post-hoc descriptions into verifiable, actionable, and comparable explanations. We call on researchers to adopt contracts in their work, conferences to encourage specification in submissions, and platforms to integrate contract templates into MAS benchmarks.

Theory · Deep Learning

Albert Alcalde, Borjan Geshkovski, Domènec Ruiz-Balet

We analyze the hardmax limit of self-attention dynamics for token embeddings in the zero-temperature regime $(\beta \to +\infty)$ and relate it to finite-$\beta$ behavior. In this limit, the update rule can be viewed as a Frank-Wolfe step for a quadratic objective over the convex hull of the current tokens. When the key-query matrix is negative semidefinite, the dynamics converge with the standard sublinear rate $\mathcal{O}(t^{-1})$ on the quadratic energy, whereas in the positive semidefinite case, extending the hardmax rule to the convex hull induces a Voronoi structure: vertices are stationary, interior points remain in their initial cells, and each token moves along a straight line toward its cell's vertex with exponential convergence under a step-size bounded away from zero. We additionally establish well-posedness of the associated ODE limit in this regime. For finite $\beta$, we model self-attention as a Markov chain and prove *dynamic metastability*: interior tokens reach near-vertex configurations in a constant number of steps and remain trapped for times exponential in $\beta$ with high probability, before eventual collapse to some point within the initial convex hull. Thus, hardmax dynamics accurately approximate the finite-$\beta$ process over exponentially long time horizons.

Applications · Language, Speech and Dialog

Yihong Tang, Kehai Chen, Liang Yue, Benyou Wang, Min zhang

Recent advancements in Reinforcement Learning (RL), particularly Group Relative Policy Optimization (GRPO), have significantly enhanced the reasoning capabilities of Large Language Models. However, applying these problem-centric optimization methods to role-playing agents often leads to a loss of character fidelity and style collapse, as they prioritize context-specific utility over persona alignment. To address this, we propose Character-Centric Group Relative Policy Optimization (CRPO), a framework designed to realign RL objectives with the role-playing task. CRPO improves character distinctiveness through three mechanisms: decoupling task logic from stylistic rewards to resolve gradient conflicts, dynamically adapting optimization constraints based on character complexity, and utilizing generic responses as negative baselines to prevent the model from reverting to a common distribution. Extensive experiments demonstrate that CRPO outperforms existing methods in consistency, emotion and others.

Reinforcement Learning · Everything Else

Hyun Kyu Lee, Joongheon Kim, Sung Whan Yoon

Robust reinforcement learning (RRL) aims to tackle unexpected environmental changes by optimizing policies against the worst case. However, RRL remains impractical due to the cost of the Max-Min optimization, where it suffers from the exhaustive query complexity for finding the worst-case (dubbed 'Min') within the environmental uncertainty set $\mathcal{U}$, i.e., $\mathcal{O}(|\mathcal{U}|)$. By viewing this via a lens of quantum perspective, we raise a pivotal question: *If we can query from the environment with quantum superpositions, is it possible to accelerate the Max-Min optimization of RRL?* Our answer is 'Yes'. Our method, called quantum robust inner minimization (QRIM), encodes the uncertainty set with quantum superposition and amplifies low-return cases, thus enabling RL for solving the robust (i.e., worst-case) Bellman equation. Importantly, QRIM achieves a quadratic speed-up in query complexity without altering the outer RL pipeline, i.e., $\mathcal{O}(\sqrt{|\mathcal{U}|})$. Validated through classical simulations to real quantum hardware execution, QRIM learns more robust policies with quadratically reduced queries than classical RL.

Social Aspects · Safety

Jianan Li, Simeng Qin, Jiapeng Chen, Lionel WANG, Tianhang Zheng, Xiaoshuang Jia, Yang Liu, Xiaochun Cao

Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in reasoning and generation tasks and are increasingly deployed in real-world applications. However, their explicit chain-of-thought (CoT) mechanism introduces new security risks, making them particularly vulnerable to jailbreak attacks. Existing approaches often rely on static CoT templates to elicit harmful outputs, but such fixed designs suffer from limited diversity, adaptability, and effectiveness. To overcome these limitations, we propose an adaptive evolutionary CoT jailbreak framework, called AE-CoT. Specifically, the method first rewrites harmful goals into mild prompts with teacher role-play and decomposes them into semantically coherent reasoning fragments to construct a pool of CoT jailbreak candidates. Then, within a structured representation space, we perform multi-generation evolutionary search, where candidate diversity is expanded through fragment-level crossover and a mutation strategy with an adaptive mutation-rate control mechanism. An independent scoring model provides graded harmfulness evaluations, and high-scoring candidates are further enhanced with a harmful CoT template to induce more destructive generations. Extensive experiments across multiple models and datasets demonstrate the effectiveness of the proposed AE-CoT, consistently outperforming state-of-the-art jailbreak methods.

Optimization · Zero-order and Black-box Optimization

Yuxuan Ren, Abhishek Roy, Shiqian Ma

Dueling optimization considers optimizing an objective with access to only a comparison oracle of the objective function. It finds important applications in emerging fields such as recommendation systems and robotics. Existing works on dueling optimization mainly focused on unconstrained problems in the Euclidean space. In this work, we study dueling optimization over Riemannian manifolds, which covers important applications that cannot be solved by existing dueling optimization algorithms. In particular, we propose a Riemannian Dueling Normalized Gradient Descent (RDNGD) method and establish its iteration complexity when the objective function is geodesically $L$-smooth or geodesically (strongly) convex. We also propose a projection-free algorithm, named Riemannian Dueling Frank–Wolfe (RDFW) method, to deal with the situation where projection is prohibited. We establish the iteration and oracle complexities for RDFW. We illustrate the effectiveness of the proposed algorithms through numerical experiments on both synthetic and real applications.

Social Aspects · Alignment

Junhyuk Choi, Sohhyung Park, chanhee cho, Hyeonchu Park, Bugeun Kim

While LLM-as-a-Judge is widely used in automated evaluation, existing validation practices primarily operate at the level of observed outputs, offering limited insight into whether LLM judges themselves function as stable and reliable measurement instruments. To address this limitation, we introduce a two-phase diagnostic framework for assessing reliability of LLM-as-a-Judge, grounded in Item Response Theory (IRT). The framework adopts Graded Response Model (GRM) of IRT and formalizes reliability along two complementary dimensions: (1) intrinsic consistency, defined as the stability of measurement behavior under prompt variations, and (2) human alignment, capturing correspondence with human quality assessments. We empirically examine diverse LLM judges with this framework, and show that leveraging IRT-GRM yields interpretable signals for diagnosing judgments systematically. These signals provide practical guidance for verifying reliablity of LLM-as-a-Judge and identifying potential causes of unreliability.

General Machine Learning · Clustering

Yifan Wei, Dan Yuan, Xiaoyan Yu, Angsheng Li

Retrieval-Augmented Generation (RAG) increasingly relies on hierarchical indexing, yet existing frameworks are bottlenecked by the high cost and information loss of recursive, LLM-based summarization. We propose SeRAG, a novel token-free hierarchical indexing framework that replaces textual summaries with an information-theoretic knowledge taxonomy. SeRAG first transforms a corpus into a multi-perspective graph capturing semantic, logical, and sequential dependencies, then minimizes structural entropy to induce a topologically-faithful encoding tree. To bridge the gap between abstract themes and granular facts, we introduce localized structural weight-based vector aggregation for token-free community consolidation. Extensive experiments demonstrate that SeRAG significantly reduces indexing overhead while outperforming state-of-the-art methods in complex multi-hop reasoning tasks.

Deep Learning · Large Language Models

Lehan He, Zeren Chen, Zhe Zhang, Xiang Gao, Lu Sheng

LLMs excel at code generation, yet ensuring the functional correctness of their outputs remains a persistent challenge. While recent studies have applied Test-Driven Development (TDD) to refine code, these methods are often undermined by poor feedback quality, stemming from the scarcity of high-quality test cases and noisy signals from auto-generated ones. In this work, we shift the focus from test quantity to feedback quality. We introduce the Property-Generated Solver (PGS), a novel paradigm designed to generate highly effective feedback via two principles: it must be property-oriented, to provide semantic guidance beyond simple I/O mismatches, and structurally minimal, to reduce cognitive load and isolate root causes. PGS operates by checking high-level program properties (e.g., a sorting function must produce a non-decreasing sequence) then providing the simplest failing counterexample to the LLM. This property-driven, minimal feedback steers LLMs toward correct and generalizable solutions. Across diverse benchmarks, PGS demonstrates superior performance, achieving a bug fix rate 1.4x-1.6x higher than the strongest debugging-based approaches and establishing a new state-of-the-art in automated code refinement. Source code and data are available in the supplementary.

Deep Learning · Large Language Models

boyu shi, Chang Liu, Chuanbao Gao, Xu Yang, Xin Geng

Layer pruning efficiently reduces Large Language Model (LLM) computational costs but often triggers sudden performance collapse. Existing representation-based analyses struggle to explain this mechanism. We propose studying pruning through decision representation. Focusing on multiple-choice tasks, we introduce two metrics, Decision Margin and Option Frequency, and an Iterative Pruning method to analyze layer-wise decision dynamics. Our findings reveal a sharp decision transition that partitions the network into two stages: a Silent Phase, where the model cannot yet predict the correct answer, and a Decisive Phase, where the correct prediction emerges. We also find that pruning the Decisive Phase has minimal impact, whereas pruning the Silent Phase triggers immediate performance collapse, highlighting its extreme sensitivity to structural changes. Therefore, we conclude that pruning-induced collapse stems from disrupting the Silent Phase, which prevents the critical decision transition from occurring.

Deep Learning · Large Language Models

Shijun Li, Hilaf Hasson, Joydeep Ghosh

Agents powered by advanced large language models (LLMs) have demonstrated impressive capabilities across diverse complex applications. Recently, Multi-Agent Systems (MAS), wherein multiple agents collaborate and communicate with each other, have exhibited enhanced capabilities in complex tasks, such as high-quality code generation and arithmetic reasoning. However, the development of such systems often relies on handcrafted methods, and the literature on systematic design and optimization of LLM-based MAS remains limited. In this work, we introduce OMAC, a general framework designed for holistic optimization of LLM-based MAS. Specifically, we identify five key optimization dimensions for MAS, encompassing both agent functionality and collaboration structure. Building upon these dimensions, we first propose a general algorithm, utilizing two actors termed the Semantic Initializer and the Contrastive Comparator, to optimize any single dimension. Then, we present an algorithm for joint optimization across multiple dimensions. Extensive experiments demonstrate the superior performance of OMAC on diverse tasks against recent approaches. Codes are available at: https://anonymous.4open.science/r/OMAC-Sub-3FF8.

Probabilistic Methods · Everything Else

Kianoosh Ashouritaklimi, Stefano Cortinovis, Francois Caron

Bayes--assisted conformal prediction combines the strengths of Bayesian modelling with exact, distribution--free frequentist coverage guarantees. While validity holds even under model misspecification, the size of the prediction sets can degrade significantly when the prior is poorly aligned with the observed data. We address this limitation by introducing \textbf{RoBAS}: a novel Bayes--assisted nonconformity score which is motivated by a hierarchical Bayesian working model with heavy--tailed priors, and which we implement in practice via a computationally tractable empirical Bayes instantiation. Our proposed method is adaptive to the quality of the available working information in the prior. When reliable prior information is available and can be effectively encoded, we achieve set sizes lower than that of other sets with the same coverage. On the other hand, when such information is weak or inaccurate, our nonconformity scores revert to the Distance--To--Average score, a robust baseline that is well--suited to settings where accurate prior information is not available. We evaluate our method on tabular and image regression tasks in the setting where there exists distribution shift between the training and calibration/test data. We find that our approach is competitive with widely used nonconformity scores in the absence of distribution shift, while providing significant gains in the more challenging setting of distribution shift.

Probabilistic Methods · Everything Else

Yating Liu, Yeo Jin Jung, Zixuan Wu, So Won Jeong, Claire Donnat

Conformal prediction provides distribution-free prediction sets with finite-sample conditional guarantees. RKHS-based frameworks—while promising for complex covariate shifts—suffer from prohibitive computational costs. To guarantee conditional validity under such shifts while ensuring feasibility, we build upon the framework of (Gibbs et al., 2023) by introducing a stable and efficient algorithm that computes the full solution path of the regularized RKHS conformal optimization problem, at essentially the same cost as a single kernel quantile fit. Our approach provides simultaneous hyperparameter tuning which provides smoothness control and data-adaptive calibration. To extend the method to high-dimensional settings, we further integrate our approach with low-rank latent embeddings that capture conditional validity in a data-driven latent space. Empirically, our method provides reliable conditional coverage across a variety of modern black-box predictors, improving the interval length of (Gibbs et al., 2023) by 30%, while achieving a 40-fold speedup.

Deep Learning · Large Language Models

Priya Pitre, Gaurav Srivastava, Lu Zhang, Le Wang, Naren Ramakrishnan, Xuan Wang

Multi-agent LLM debates are increasingly deployed in domains such as policy analysis and city planning, where no objective ground truth exists. Despite this, debate quality is typically evaluated using outcome-based proxies such as LLM-as-judge scores that provide little insight into whether meaningful deliberation has occurred. Additionally, consensus and majority vote are viewed as ideal goals without analyzing the underlying interaction dynamics beneath them. In this work, we introduce a diagnostic evaluation framework that measures debate quality by measuring both the outcome and the process. Grounded in deliberative theory, our framework defines four interpretable process-level metrics capturing engagement, responsiveness, influence asymmetry, and balance, and two outcome-based metrics capturing stability and agent utility. Across both objective benchmarks and real-world domains, we find that process-level diagnostics are consistently more informative than commonly used outcome-based proxies. They better reflect correctness when ground truth exists and align more closely with human judgments of deliberative quality when it does not, revealing interaction failures that outcome-only measures fail to capture. These results demonstrate that process-level diagnostics are necessary for reliable evaluation of multi-agent debates and provide a principled foundation for analyzing and designing deliberative LLM systems.