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General Machine Learning · Scalable Algorithms

Felix X.-F. Ye, Xingjie Li, An Yu, Ming-Ching Chang, LINSONG CHU, Davis Wertheimer

Entropic optimal transport (EOT) via Sinkhorn iterations is widely used in modern machine learning, yet GPU solvers remain inefficient at scale. Tensorized implementations suffer quadratic HBM traffic from dense $n\times m$ interactions, while existing online backends avoid storing dense matrices but still rely on generic tiled map-reduce reduction kernels with limited fusion. We present **FlashSinkhorn**, an IO-aware EOT solver for squared Euclidean cost that rewrites stabilized log-domain Sinkhorn updates as row-wise LogSumExp reductions of biased dot-product scores, the same normalization as transformer attention. This enables FlashAttention-style fusion and tiling: fused Triton kernels stream tiles through on-chip SRAM and update dual potentials in a single pass, substantially reducing HBM IO per iteration while retaining linear-memory operations. We further provide streaming kernels for transport application, enabling scalable first- and second-order optimization. On A100 GPUs, FlashSinkhorn achieves up to $32\times$ forward-pass and $161\times$ end-to-end speedups over state-of-the-art online baselines on point-cloud OT, improves scalability on OT-based downstream tasks.

Applications · Robotics

Junhao Shi, Siyin Wang, Xiaopeng Yu, Li Ji, Jingjing Gong, Xipeng Qiu

Vision-Language-Action (VLA) models are bottlenecked by the scarcity of expert demonstrations—expensive triplets of observations, language instructions, and actions. We propose that learning ''how to move'' can be decoupled from learning ''what to do,'' and that the former requires no task labels at all. Our two-stage framework, **Task-Agnostic Pretraining (TAP)** first pre-trains on abundant, cheap *task-agnostic* data (discarded off-task trajectories or autonomous robot play) using an Inverse Dynamics objective that predicts actions from consecutive observations. This self-supervised phase instills physical affordances—grasping, contact dynamics, end-effector control—without human annotation. A lightweight second stage then aligns these physical priors with language instructions using minimal expert data. On the SIMPLER benchmark, our approach matches models trained on 1M+ expert trajectories while using orders of magnitude less labeled data, achieving a 10\% absolute gain over standard behavior cloning. In real-world WidowX experiments, it surpasses internet-scale baselines under visual distribution shifts (e.g., 25\% vs. 0\% under camera perturbations), demonstrating that task-agnostic pretraining yields robust, transferable physical representations for Embodied AI.

Deep Learning · Generative Models and Autoencoders

Jinyan Ye, Zhongjie Duan, Zhiwen Li, Cen Chen, Daoyuan Chen, Yaliang Li, Yingda Chen

Inference-time scaling offers a versatile paradigm for aligning visual generative models with downstream objectives without parameter updates. However, existing approaches that optimize the high-dimensional initial noise suffer from severe inefficiency, as many search directions exert negligible influence on the final generation. We show that this inefficiency is closely related to a spectral bias in generative dynamics: model sensitivity to initial perturbations diminishes rapidly as frequency increases. Building on this insight, we propose Spectral Evolution Search (SES), a plug-and-play framework for initial noise optimization that executes gradient-free evolutionary search within a low-frequency subspace. Theoretically, we derive the Spectral Scaling Prediction from perturbation propagation dynamics, which explains the systematic differences in the impact of perturbations across frequencies. Extensive experiments demonstrate that SES significantly advances the Pareto frontier of generation quality versus computational cost, consistently outperforming strong baselines under equivalent budgets. Our code is available at \url{https://anonymous.4open.science/r/Spectral-Evolution-Search-66DB}.

Deep Learning · Large Language Models

Gengsheng Li, Jinghan He, Shijie Wang, Dan Zhang, Ruiqi Liu, Renrui Zhang, Zijun Yao, Junfeng Fang, Haiyun Guo, Jinqiao Wang

Self-play bootstraps LLM reasoning through an iterative Challenger–Solver loop: the Challenger is trained to generate questions that target the Solver's capabilities, and the Solver is optimized on the generated data to expand its reasoning skills. However, existing frameworks like R-Zero often exhibit non-sustained improvement, where early gains degrade as self-play continues. We identify a key failure mode, Diversity Illusion, where the Solver's training signals appear diverse yet collapse into recurring underlying patterns. It manifests as (1) Local Diversity Illusion, where diversity is enforced only within-batch, inducing cross-iteration mode cycling; and (2) Surface Diversity Illusion, where questions vary superficially but require near-identical reasoning skills. To mitigate them, we propose R-Diverse with two aligned innovations: Memory-Augmented Penalty (MAP), which uses a persistent memory bank to discourage recycling across iterations, and Skill-Aware Measurement (SAM), which evaluates diversity by the reasoning skills exercised rather than surface variation of questions. Across 10 math and general reasoning benchmarks, R-Diverse sustains gains over more iterations and consistently outperforms prior self-play methods.

Deep Learning · Large Language Models

Tomás Vergara Browne, Darshan Patil, Ivan Titov, Siva Reddy, Tiago Pimentel, Marius Mosbach

The superficial alignment hypothesis (SAH) posits that large language models learn most of their knowledge during pre-training, and that post-training merely surfaces this knowledge. The SAH, however, lacks a precise definition, which has led to (i) different and seemingly orthogonal arguments supporting it, and (ii) important critiques to it. We propose a new metric called **Task Complexity**: the length of the shortest program that achieves a target performance on a task. In this framework, the SAH claims that pre-trained models drastically reduce the task complexity of achieving high performance on many tasks. Our definition unifies prior arguments supporting the SAH, interpreting them as different strategies to find such short programs. Experimentally, we estimate task complexities of mathematical reasoning, machine translation, and instruction following tasks and show that their respective task complexities can be remarkably low when conditioned on a pre-trained model. Further, we find that pre-training enables access to strong performances on our tasks, but it can require programs of gigabytes of length to access them. Post-training, on the other hand, collapses the complexity of reaching this same performance by several orders of magnitude. Overall, our results highlight that task adaptation can require remarkably little information—often just a few kilobytes.

Deep Learning · Large Language Models

HyunJin Kim, Jaejun Shim, Young Jin Kim, JinYeong Bak

Unsupervised dense retrievers offer scalability by learning semantic similarity from unlabeled documents via contrastive learning, but they struggle to capture the temporal relevance, retrieving semantically related but temporally misaligned documents-an important aspect when a document collection spans multiple time periods (e.g., retrieving from related document spanning 2018-2025 given a query "Who is the president in 2019?'' introduces temporal ambiguity). Existing methods rely on supervised training with explicit timestamps, which are not always feasible.We propose TPOUR (*Temporal Preference Optimization for Unsupervised Retriever*), which integrates our novel training method *Temporal Retrieval Preference Optimization* (TRPO). TRPO reinterprets preference learning in the temporal dimension, guiding the retriever to favor temporally aligned documents. TPOUR further generalizes to unseen time periods via interpolation in a learned time embedding, enabling continuous temporal alignment. Experiments on temporal QA with a mixed-timestamp document collection show that TPOUR outperforms both unsupervised and supervised baselines. Compared to Nomic Embed v2 MoE, TPOUR Contriever improves nDCG@5 by +7.13 (+23.5%) on explicit and +7.76 (+25.5%) on implicit queries on average.

Theory · Reinforcement Learning and Planning

Jose Aguilar Escamilla, Haoyang Hong, Jiawei Li, Haoyu Zhao, Xuezhou Zhang, Sanghyun Hong, Huazheng Wang

We study reward poisoning attacks in reinforcement learning (RL), where an adversary manipulates rewards within constrained budgets to force the target RL agent to adopt a policy that aligns with the attacker's objectives. Prior works on reward poisoning mainly focused on sufficient conditions to design a successful attacker, while only a few studies discussed the infeasibility of targeted attacks. This paper provides the first precise necessity and sufficiency characterization of the attackability of a linear MDP under reward poisoning attacks. Our characterization draws a bright line between the vulnerable RL instances, and the intrinsically robust ones which cannot be attacked without large costs even running vanilla non-robust RL algorithms. Our theory extends beyond linear MDPs---by approximating deep RL environments as linear MDPs, we show that our theoretical framework effectively distinguishes the attackability and efficiently attacks the vulnerable ones, demonstrating both the theoretical and practical significance of our characterization.

Applications · Computer Vision

Borui Kang, Jinrui Gu, Tao Feng, Qi Fan, Yinghuan Shi, Lei Wang, Wenbin Li, Yang Gao

Continual learning of Vision-Language Model (VLM) aspires to empower foundation models with new expertise without compromising their universal zero-shot capabilities. However, this pursuit faces a critical ''dual-forgetting'' challenge: the catastrophic forgetting of newly acquired classes (Incremental Knowledge Forgetting, IKF) and the insidious erosion of foundational zero-shot capabilities (Pre-trained Knowledge Forgetting, PKF). Existing evaluations often ignore PKF or assess it via confounded protocols where positive transfer on semantically similar domains creates an illusion of retention, masking severe foundational degradation. To address this, we propose the Dual-Forgetting-Aware Class-Incremental Learning (DFA-CIL) framework and the Similarity-Calibrated Retention (SCR) metric. Unlike standard averaging, SCR utilizes the frozen pre-trained feature space to inversely weight performance based on semantic similarity, effectively mitigating the confounding gains to stress-test foundational stability. Building on this, we propose DFA-MoE, a functionally heterogeneous Parameter-Efficient Fine-Tuning (PEFT) method. DFA-MoE architecturally decouples optimization objectives by assigning a momentum-enhanced contrastive expert for feature alignment, and separate plasticity experts that combine classification with auxiliary contrastive learning to adapt to new tasks while retaining historical knowledge. Extensive experiments demonstrate that our framework uncovers the hidden fragility of existing methods and achieves a state-of-the-art balance in preserving both incremental and pre-trained knowledge.

Social Aspects · Alignment

Yonghui Yang, Wenjian Tao, Jilong Liu, Xingyu Zhu, Junfeng Fang, Huang Weibiao, Le Wu, Richang Hong, Tat-Seng Chua

Safety alignment remains brittle under domain shift and noisy preference supervision. Existing robust alignment methods predominantly focus on data uncertainty in alignment data, while being less effective at addressing failures caused by optimization-induced fragility. In this work, we revisit robustness for safety alignment from an optimization geometry perspective, highlighting optimization-induced fragility as a complementary factor to data-space uncertainty. To address this, we propose ShaPO, a geometry-aware preference optimization framework that enforces worst-case alignment objectives via selective geometry control. We instantiate ShaPO at two levels: Token-level ShaPO stabilizes likelihood-based surrogate optimization, while Reward-level ShaPO enforces reward-consistent optimization and provides improved robustness under noisy supervision. Across diverse safety benchmarks and noisy preference settings, ShaPO consistently improves safety robustness over popular preference optimization methods. Moreover, ShaPO composes cleanly with data-robust objectives, yielding additional gains and empirically supporting the proposed optimization-geometry perspective. Our results highlight optimization geometry as a complementary and actionable axis for robust LLM safety alignment. The code is available at \url{https://anonymous.4open.science/r/ShaPO-D1B0}.

Deep Learning · Generative Models and Autoencoders

Sayedmoslem Shokrolahi, Jae-Mo Kang, Il-Min Kim

Text-to-image (T2I) diffusion models (DMs) have achieved remarkable generative quality but still exhibit the risk to produce biased and inappropriate images. A promising line of prior work aims to mitigate this issue by learning interpretable and linearly controllable concepts from semantic spaces, such as the U-Net bottleneck. However, these methods rely entirely on the bottleneck layer in U-Net and therefore cannot be directly applied to modern ViT-based DMs, including FLUX and PixArt. In this work, we present a model agnostic framework for discovering interpretable and linearly controllable semantic attributes across any T2I DMs backbone. We first show that multi-modal attention heads in ViT-based DMs encode interpretable and (near) linear semantic structures similar to those in the U-Net bottleneck. Based on this insight, we introduce a method that learns external concept vectors, which are added to the multi-modal attention heads for ViT-based DMs or to the bottleneck layer for U-Net-based DMs, while keeping the pretrained model frozen. Experiments across SDXL, SD3.5, PixArt, and FLUX demonstrate that these concept vectors provide interpretability, linearity, and highly improved fairness while preserving visual fidelity. The code is included in the supplementary material.

Applications · Everything Else

Ali Noshad, Zishan Zheng, Yinjun Wu

To reduce LLM costs and latency, semantic caching systems must accurately identify when a new prompt matches a cached one. Current methods often rely on simplistic similarity measures, which limit their effectiveness. We introduce MVR-cache, a novel semantic caching approach that significantly improves retrieval accuracy by integrating Multi-Vector Retrieval (MVR). MVR-cache is built upon a learnable segmentation model that intelligently splits prompts, enabling fine-grained similarity comparisons via MaxSim. We derive the model’s training objectives from a rigorous theoretical analysis. This can ensure that optimizing these objectives directly maximizes cache hits under strict correctness constraints. To solve the resulting non-differentiable combinatorial optimization problem, we leverage a reinforcement learning-based training strategy with the theoretically grounded objectives as the reward. Experimental results on established benchmarks across diverse tasks confirm that in comparison to the state-of-the-art, MVR-cache consistently increases the cache hit rates by up to 25% while maintaining the same correctness guarantees.

Applications · Neuroscience, Cognitive Science

Shunchi Zhang, Jin Lu, Chuanyang Jin, Yichao Zhou, Zhining Zhang, Tianmin Shu

Effective real-world assistance requires AI agents with robust Theory of Mind (ToM): inferring human mental states from their behavior. Despite recent advances, several key challenges remain, including (1) online inference with robust uncertainty updates over multiple hypotheses; (2) efficient reasoning suitable for real-time assistance; and (3) the lack of ground-truth mental state annotations in real-world domains. We address these challenges by introducing *MindZero*, a self-supervised reinforcement learning framework that trains language models to perform efficient and robust online mental reasoning. During training, the model is rewarded for generating mental state hypotheses that maximize the likelihood of observed actions estimated by a planner, similar to model-based ToM reasoning. This method thus eliminates the need for explicit mental state annotations. After training, *MindZero* internalizes model-based reasoning, and performs mental inference in a single forward pass at test time. We evaluate *MindZero* in four challenging mental reasoning and AI assistance domains. *MindZero* matches the robustness of explicit model-based methods while significantly accelerating inference, outperforming state-of-the-art methods by a large margin. These results demonstrate that mental reasoning can be learned as a self-supervised skill, bridging the gap between robustness and efficiency in ToM modeling.

Deep Learning · Large Language Models

Shuaidi Wang, Zhan Zhuang, HUANG Ruping, Yu Zhang

Diffusion Large Language Models (dLLMs) have emerged as a promising non-autoregressive generative paradigm. Given the prohibitive computational cost of full fine-tuning, Parameter-Efficient Fine-Tuning (PEFT) has become the standard approach. However, existing PEFT methods (e.g., LoRA), originally tailored for autoregressive models, rely on static parameters that are agnostic to the noise level. Consequently, they ignore the intrinsic dynamics of the diffusion process, where input distributions and generation difficulty shift significantly along the denoising trajectory, rendering them suboptimal for dLLMs. To address this, we propose **N**oise-**a**ware Low-**R**ank **A**daptation (NaRA), which introduces a low-rank core matrix generated by a lightweight, globally shared hypernetwork conditioned on the noise level. This design enables the update matrices to vary continuously along the diffusion process while keeping parameter and latency overhead negligible. We provide a theoretical justification for the proposed NaRA framework and empirically demonstrate consistent improvements over noise-agnostic baselines across commonsense reasoning, mathematical reasoning, and code generation benchmarks.

General Machine Learning · Evaluation

Yuanzhi Liu, Shousheng Zhao, Bo Zhou, Kongming Liang, Zhanyu Ma

Evaluation benchmarks play a central role in assessing vision–language models (VLMs). However, most existing multimodal benchmarks are static, making them increasingly vulnerable to data contamination, temporal staleness, and high construction costs. In this work, we introduce MMBench-Live, a multi-agent-driven dynamic multimodal benchmark that supports continuous updates without human in the loop. MMBench-Live is maintained through an end-to-end automated pipeline that integrates structured benchmark description, real-time data acquisition, and verifiable question–answer (QA) generation, enabling scalable, live, and low-cost benchmark updates. To ensure reliable evaluation across versions, we further propose a distribution-consistent updating strategy based on semantic task interpretation and feedback-driven data collection and filtering. Based on MMBench-Live, we conduct systematic evaluations of multiple open-source VLMs and analyze their performance, cross-version consistency, and data contamination, providing empirical evidence for the effectiveness of the proposed dynamic benchmark updating framework.

Deep Learning · Large Language Models

Kaustubh Vijaykumar Pethkar, Ziyang Xiong, Zuofeng Shang, Yingcong Li

Continual incorporation of new knowledge is essential for the long-term evolution of large language models (LLMs). Existing approaches typically rely on parameter-update algorithms to mitigate catastrophic forgetting, yet they suffer from fundamental limitations: 1) forgetting is unavoidable as the amount of newly injected knowledge grows; and 2) model updates are often irreversible. As modern LLMs become increasingly expressive, it is natural to question whether large-scale weight updates are necessary for acquiring a small amount of new knowledge. In this work, we propose a principled framework that models autoregressive language generation as a Markov process over tokens, where model memory is represented by a Markov transition matrix. Under this formulation, incorporating new knowledge/tokens corresponds to extending the state space, and preserving existing transitions guarantees retention of previously learned knowledge. We then prove a sample complexity bound for incorporating new tokens via a token-to-dictionary mapping strategy. In particular, for learning the transition behavior of each new token, the required number of samples scales linearly with the number of existing tokens it is mapped to. To realize this mapping, we propose an embedding-tuning algorithm that requires minimal parameter updates and induces zero forgetting. Experimental results further demonstrate the effectiveness of our method and validate our theoretical findings.

Social Aspects · Accountability, Transparency, and Interpretability

Liuyuan Wen, Xun Zhu, Lihao Huang, Wenbin Li, Yang Gao

Large Language Models exhibit paradoxical fragility in fundamental arithmetic, implying a disconnect between internal computation and discrete output. By analyzing the residual stream geometry during multi-operand addition, we identify the **Iso-Raw-Sum Trajectory (IRST)**, a topological manifold where representations are anchored by semantic digits and modulated by continuous carry fibers. We propose the **Noisy Quantization Model**, which frames arithmetic errors as *topological slippages* caused by internal neural noise pushing a continuous, latent *carry potential* across quantization thresholds. This geometric framework further elucidates *probe versatility*, explaining how lightweight probes can disentangle conflicting latent signals (such as ground truth versus hallucination) from a single activation vector. Finally, we validate these insights through a geometric consistency check method that effectively detects and corrects these quantization failures during inference.

Ali Rad, Khashayar Filom, Darioush Keivan, Peyman Mohajerin Esfahani, Ehsan Kamalinejad

Reinforcement learning with verifiable rewards (RLVR) trains a policy by verifying sampled completions and reinforcing higher-scoring outputs, but practical verifiers (e.g., incomplete unit tests or noisy judges) are prone to false positives and false negatives. We ask when such noise merely slows learning and when it reverses it. Modeling GRPO-style RLVR as a bandit over recurring \emph{reasoning modes}, we derive mean-field replicator-style (natural-selection) flow on the probability simplex. The dynamics decouples into within-correct-mode competition and a one-dimensional evolution for the mass on incorrect modes, whose drift is determined solely by Youden's index $J=\mathrm{TPR}-\mathrm{FPR}$. This yields a sharp phase transition: when $J>0$, the incorrect mass is driven toward extinction (learning); when $J=0$, the process is neutral; and when $J<0$, incorrect modes amplify until they dominate (anti-learning and collapse). In the learning regime $J>0$, noise primarily rescales convergence time (``rate, not fate''). Experiments on verifiable programming tasks under synthetic noise reproduce the predicted $J=0$ boundary. Beyond noise, the framework offers a general lens for analyzing RLVR stability, convergence, and algorithmic interventions.

Deep Learning · Large Language Models

Xiangyang Zhu, Yuan Tian, Qi Jia, Kaiwei Zhang, Zicheng Zhang, Chunyi Li, Zijian Chen, Lu Sun, Kaiyuan Ji, Renrui Zhang 等

The success of large language models (LLMs) in scientific domains has heightened safety concerns, prompting numerous benchmarks to evaluate their scientific safety. Existing benchmarks often suffer from limited risk coverage and a reliance on subjective evaluation. To address thess problems, we introduce \textbf{SafeSci}, a comprehensive framework for safety evaluation and enhancement in scientific contexts. SafeSci comprises \textbf{SafeSciBench}, a multi-disciplinary benchmark with 0.25M samples, and \textbf{SafeSciTrain}, a large-scale dataset containing 1.5M samples for safety enhancement. SafeSciBench distinguishes between safety knowledge and risk to cover extensive scopes and employs objective metrics such as deterministically answerable questions to mitigate evaluation bias. We evaluate 21 advanced LLMs, revealing critical vulnerabilities in current models. Finally, we demonstrate that fine-tuning on SafeSciTrain significantly enhances the safety alignment of models. Our work provides both a diagnostic tool and a practical resource for building safer scientific AI systems.

Houcheng Jiang, Junfeng Fang, Jiaxin Wu, Tianyu Zhang, Chen Gao, Xiang Wang, Xiangnan He, Yang Deng

Weak-to-strong generalization provides a promising paradigm for scaling large language models (LLMs) by training stronger models on samples from aligned weaker ones, without requiring human feedback or explicit reward modeling. However, its robustness and generalization are hindered by the noise and biases in weak-model outputs, which limit its applicability in practice. To address this challenge, we leverage implicit rewards, which approximate explicit rewards through log-likelihood ratios, and reveal their structural equivalence with Contrastive Decoding (CD), a decoding strategy shown to reduce noise in LLM generation. Building on this connection, we propose \textbf{Contrastive Weak-to-Strong Generalization (ConG)}, a framework that employs contrastive decoding between pre- and post-alignment weak models to generate higher-quality samples. This approach enables more reliable capability transfer, denoising, and improved robustness, substantially mitigating the limitations of traditional weak-to-strong methods. Empirical results across different model families confirm consistent improvements, demonstrating the generality and effectiveness of ConG. Taken together, our findings highlight the potential of ConG to advance weak-to-strong generalization and provide a promising pathway toward AGI. Our code is available at: \url{https://anonymous.4open.science/r/ConG/}

Probabilistic Methods · Graphical Models

Mustafa Seddiqi, Tiberiu Popa, Marta Kersten-Oertel

Learning effective value heuristics for combinatorial search is difficult, as prior methods rely on surrogate supervision or costly downstream search to assess progress. We introduce a trajectory-aware probabilistic framework that models uncertainty in cost-to-go labels instead of treating them as fixed targets. Heuristic learning is cast as inference over state trajectories using an HMM-style model (Rabiner, 1989), where estimated depth-change dynamics define transitions and forward–backward inference yields soft supervision. To evaluate heuristic quality without search, we propose a large-scale local ranking metric that measures a model’s ability to order neighboring states. On the Rubik’s Cube, our approach consistently improves local ranking accuracy and downstream search performance under matched computational budgets.