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新论文 · v1arXiv:2611.16647

InertiaMPDD: Multimodal Personalized Depression Detection via Emotional Inertia-Inspired Dynamics Modeling

Changzeng Fu, Songyang Wang, Chang Gao, Xinyi Zhang, Peilin Li, Mo Meng, Jiarui Wu, Mingze Li 等 12 位作者

原文摘要

Multimodal depression screening estimates risk from speech, visual behavior and language. Current methods mainly predict labels, with limited evidence of departures from a non-depressed reference. Recording-level supervision also leaves fine-grained emotional dynamics unannotated. Motivated by emotional inertia, the persistence of affect, InertiaMPDD learns healthy next-state patterns using a world-model-inspired latent predictor. Using pretrained multimodal representations, it predicts from observed history and context. Reference deviation, residual decay rate and a Jacobian spectral descriptor form reference-derived emotional dynamics descriptors, evaluated as candidate proxy biomarkers for standalone screening and personalized decision correction. Across five cohorts from four datasets, fusion reaches 98.41% F1 on D-Vlog and 84.98% on LMVD. Standalone descriptors outperform fixed additive fusion on MPDD but discriminate weakly on AVEC2014. These findings distinguish reference informativeness from fusion benefit and reveal context-dependent utility.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15000

QSim-Arena: an evolving developer for quantum simulation advantage

Jue Xu, Yue Wang, Qi Zhao

原文摘要

Where practical advantage emerges in quantum dynamics simulation remains a central debate in quantum computing. Claimed advantages can be rapidly narrowed or overturned by stronger classical methods, while progress on both sides can outpace the journal publication cycle. We present QSim-Arena, a research framework in which AI agents combine a closed loop of verification and benchmarking with open exploration. Hamiltonians, initial states and accuracy targets for evolved states or observable expectation values provide explicit specifications for evaluating simulation methods. Agents translate published techniques into executable components, reproduce reported results and benchmark their performance across diverse Hamiltonian families. Competing classical methods are implemented and independently crosschecked, providing credible baselines for end-to-end quantum resource estimates based on rigorous error bounds and empirical calibration. Alongside this closed loop, agents recombine classical simulation techniques and co-design fault-tolerant quantum circuits using knowledge extracted from the literature. Initial applications improve baseline resource estimates through the integration of established techniques. QSim-Arena thus treats quantum advantage assessment as an evolving, reproducible research process that can respond to advances in algorithms, error analysis and hardware.

quant-ph · cs.AI

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2605.12374

Fill the GAP: A Granular Alignment Paradigm for Visual Reasoning in Multimodal Large Language Models

Yanting Miao, Yutao Sun, Dexin Wang, Mengyu Zhou, Pascal Poupart, Lei Lv, Qi Zhao, Li Wang 等 11 位作者

原文摘要

Visual latent reasoning lets a multimodal large language model (MLLM) create intermediate visual evidence as continuous tokens, avoiding external tools or image generators. However, existing methods usually follow an output-as-input latent paradigm and yield unstable gains. We identify a feature-space mismatch behind this instability: dominant visual-latent models build on pre-norm MLLMs and reuse decoder hidden states as predicted latent inputs, even though these states occupy a substantially different norm regime from the input embeddings the model was trained to consume. This mismatch makes direct latent feedback unreliable. Motivated by this diagnosis, we propose GAP, a Granular Alignment Paradigm for visual latent modeling. GAP aligns visual latent reasoning at three levels: data-level alignment grounds latent targets with inspectable auxiliary visual supervision; feature-level alignment maps decoder outputs into input-compatible visual latents through a lightweight PCA-aligned latent head; and model-level alignment assigns latent supervision selectively to examples where the base MLLM struggles. On Qwen2.5-VL 7B, GAP achieves the best aggregate perception and reasoning scores among all compared methods and is the only evaluated latent method that improves both over the base model. Inference-time interventions further show that generated latents carry task-relevant visual signal rather than merely adding token slots. Code is available at https://github.com/Qwen-Applications/GAP.

cs.CV · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.15790

Fairness-Aware Retrieval Optimization for Retrieval-Augmented Generation

Yingqi Zhao, Vasilis Efthymiou, Jyrki Nummenmaa, Kostas Stefanidis

原文摘要

Retrieval-Augmented Generation (RAG) grounds large language models (LLMs) in external knowledge, but the retrieved documents can systematically skew generated answers toward one group. This effect is difficult to analyze and control in top-k settings, where several documents at different context positions jointly shape the output. We present a three-stage framework for fairness-aware retrieval in top-k RAG. First, a probabilistic reranker sets the group composition of the retrieved context at each position, enabling controlled perturbation of retrieval bias without modifying the retriever. Second, we show that output bias is well approximated by a position-weighted linear function of position-wise retrieval bias, revealing model-specific positional sensitivities. Third, we use this model to formulate retrieval as an optimization problem that trades off relevance against predicted output bias, and propose FARO, a scalable solution to that problem which decomposes the globally coupled quadratic objective into independent per-query assignment problems through a Fenchel--Legendre dual reformulation. Experiments with four open LLMs, and political and gender bias as use cases show that the linear model predicts output bias on unseen configurations in 17 of 24 settings. FARO substantially reduces output bias in most settings, while, compared with a linear-programming baseline, FARO achieves similar relevance, remains feasible where the baseline does not, and scales better to larger query sets and candidate pools.

cs.DB · cs.IR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.08487

User Embeddings are Superpositions of Interpretable Behavioral Modes

Jonathan Kozaczuk, Zheng Dong, Noyan Evirgen, Keivan Majidi, Eddie K. H. Ng, Chris Porter, Qi Zhao, Jia Geng 等 10 位作者

原文摘要

User embeddings are central components of modern recommendation, personalization, and behavioral modeling systems. Though these embeddings can compactly encode rich behavioral information, they are inherently dense and opaque, limiting interpretability. To address this gap, we argue that user embeddings can be understood as sparse superpositions of interpretable behavioral modes. This structure is directly predicted by generative models of user behavior for linear embeddings and extends approximately to deep-learning representations by the superposition hypothesis. Superimposed modes can naturally be recovered by sparse dictionary learning methods, such as sparse autoencoders (SAEs), and recovered modes can be automatically named and queried in natural language. We validate mode recovery against known ground truth on synthetic data, where SAEs recover modes better than classical sparse baselines. We then apply our methods to classical and transformer-based user embeddings from two real-world datasets. We also show how the corresponding natural-language labels enable zero-shot audience retrieval and characterization over embeddings never trained on text. The framework is post-hoc and model-agnostic, requiring no modification to the upstream embedding system.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.06956

Octopus: One Brain, Many Branches for Latent-KV Multi-Agent Reinforcement Learning

Tianming Sha, Junnan Li, Siqi Zhao

原文摘要

Deep search and long-horizon reasoning unfold as branching trajectories that pursue parallel sub-queries, abandon dead-end detours, and cross-check sibling findings. Yet modern serving engines, harnesses, and RL trainers execute episodes as flat, append-only sequences: stretching a context window leaves failed detours that dilute attention, in-place edits break KV caching and recurrent states, and all-layer cache splicing overwrites hybrid recurrent working memory and triggers hijacking. We introduce Octopus, a co-designed serving runtime, memory manager, and RL framework executing episodes as a GPU-resident DAG over radix cursors (89.6% KV hit rate). Agents coordinate via five primitives: fork shares parent KV prefixes in $O(1)$ with isolated 32K budgets, rewind restores recurrent checkpoints, read_thread mounts sibling KV pages in zero copy, and wait and done synchronize results. Octopus splices sibling KV pages only into full-attention layers under monotonic position offsets while recurrent layers skip mounted spans, retaining 92.2% of full-text answer information and cutting hijacking $21\times$ ($57.9\% \to 2.7\%$) with zero new KV pages. During RL, our packed DAG kernel and DAG-GRPO objective deduplicate shared prefixes via loss masks while keeping mounted KV edges differentiable, cutting rollout-training KL divergence $32\times$ and routing reader gradients into producer KV projections. Trained on Qwen3.5-4B with binary rewards, Octopus self-organizes a commander-worker hierarchy and latent KV mesh, lifting BrowseComp-Plus accuracy under BM25 from 26.0% (32K ReAct) and 32.1% (143K GRPO) to 49.6% (+17.5 pts; 51.2% on OpenResearcher), surpassing 9B context-editing RL (42.5%), matching o3 (49.3%), and outperforming dense-retrieval agents up to $250\times$ larger: Tongyi-DeepResearch (44.5%), DeepSeek-R1 (16.4%), Kimi-K2 (35.4%), GPT-4.1 (36.4%), and Claude 4 Opus (36.8%).

cs.LG · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08912

SSF-Net: A Spatial-Spectral Fusion Network for Accurate PDE Operator Learning

Siqi Zhao, Flora D. Salim

原文摘要

Machine learning methods for solving partial differential equations (PDEs) significantly improve computational efficiency over traditional numerical solvers such as finite element method (FEM). However, PDE dynamics inherently span both local and global dynamics: disturbances propagate and decay gradually through the domain, while global coupling effects can influence the whole system simultaneously. Capturing both types of dynamics with a low complexity model remains a key challenge. To address this, we propose the Spatial-Spectral Fusion Network (SSF-Net), built around a dual-branch Spatial-Spectral Fusion (SSF) block comprising a spatial branch and a spectral branch. The spatial branch employs a multi-directional Mamba, which mitigates its inherent causal, single-direction scanning bias and capture local interactions with reduced directional bias at linear complexity. The spectral branch leverages a Fourier transform with a block-diagonal MLP to efficiently capture global spectral features with reduced parameter count and complexity. The SSF block is embedded in a multiresolution convolutional encoder-decoder, which extracts multi-scale features. We evaluate SSF-Net on a comprehensive PDE benchmark spanning multiple task types and assess its long-horizon stability. Compared with attention-based and convolution-based baselines, SSF-Net achieves lower prediction error with fewer parameters while retaining near-linear asymptotic complexity in the number of spatial tokens.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08486

生成式AI时代下，我们是否怀念那些精心制作图表的夜晚？对Teaser图与框架图的再思考

Do You Miss the Nights We Spent Crafting Figures? Rethinking Teaser and Framework Figures in the Age of Generative AI

Xiaoqi Zhao, Youwei Pang, Kailai Zhou

AI 速读 基于摘要

作者通过对200篇论文中371幅动机图与框架图的视觉回顾和源约束修订实验，探讨生成式AI辅助科研图表设计时解释清晰度与视觉表达之间的权衡。

cs.CV

arXiv ↗PDF ↗
查看原文摘要

Scientific figures give research ideas a visual form through choices of abstraction, composition, and emphasis. Generative AI offers new ways to realize these choices, raising questions about explanatory clarity and purposeful visual expression. We examine these questions through a visual retrospective of 371 motivation and framework figures from 200 papers. The corpus comprises a quota-based main sample of 160 papers spanning four temporal periods and a separate purposive style-contrast sample of 40 papers. A single multimodal judge evaluates six dimensions of communication and design, with three repeated judgments per figure. We attempt source-constrained revisions of all 371 figures, of which 369 pass the recorded fidelity checks. Original-figure ratings show no uniform decline across periods. Revisions increase mean clarity ratings by 0.428 points and decrease aesthetic coherence ratings by 0.111 points on a five-point scale. Gains in the six-dimension average are larger for main-sample motivation figures than for frameworks. Case analyses show that added explanation can clarify comparisons while leaving typographic hierarchy and visual economy unresolved. These model-based findings support researcher-led design, with AI assisting exploration and authors retaining responsibility for explanatory purpose, composition, and scientific relationships. The accompanying atlas and evaluation records are available at https://ntu-ai4x.github.io/Rethinking_Figures/index_en.html.

版本更新 · v2arXiv:2611.06777

WISE: An Agentic Framework for Whole-page Multimodal Search Experience Attribution

Wenyi Xu, Renzhe Zhou, Songyang Li, Feiran Zhu, Ruoqi Zhao, Chenglei Dai, Xuanping Li, Kaiqiao Zhan 等 9 位作者

原文摘要

Algorithm-driven services, including search, recommendation, and advertising, rely on multi-stage model pipelines. While research prioritizes model optimization, post-deployment monitoring focuses on operational reliability. Yet operationally healthy systems may still produce poor-quality results, exposing a gap in existing evaluation and monitoring practices. However, a systematic problem formulation and benchmark for this task remain lacking. To bridge this gap, we formulate System Quality Diagnosis (SQD) for algorithm-driven services, a new task that extends quality evaluation beyond identifying poor outcomes to attributing their causes across system components. Focusing on industrial search, we propose WISE, an agentic framework for end-to-end quality diagnosis that discovers user-facing issues and traces their origins to underlying algorithmic components. To improve diagnostic reliability, we introduce ReTrace for constructing evidence-consistent trajectory supervision and Adaptive Harness-Policy Optimization (AHPO) for failure-guided optimization of the agent harness and model policy. To enable systematic evaluation, we construct WISE-BENCH, a real-world multimodal search benchmark that combines user-facing evidence, internal pipeline traces, and expert diagnosis annotations. Extensive experiments show consistent gains across model scales, with WISE achieving 75.20% end-to-end accuracy on Qwen3.5-35B-A3B. In production, WISE supports quality auditing, online quality assurance, and model optimization, reducing diagnosis costs and improving downstream relevance models.

cs.IR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2508.12969

Compact Attention: Exploiting Structured Spatio-Temporal Sparsity for Fast Video Generation

Qirui Li, Guangcong Zheng, Qi Zhao, Jie Li, Bin Dong, Yiwu Yao, Xuewei Li, Zequn Qin 等 9 位作者

原文摘要

The quadratic computational complexity of self-attention mechanisms imposes a critical constraint on transformer-based video generation, particularly for synthesizing ultra-long and high-definition sequences. While most sparse attention techniques employ fixed or heuristic patterns, they often struggle to accommodate the heterogeneous and layer-specific redundancies inherent in video diffusion transformers (VDiTs). We find that different attention heads consistently concentrate most of their attention mass in different coarse query-key regions, a behavior we call functional specialization. Although individual attention values vary with the generated content, these regions remain similar across prompts and random seeds. Leveraging these insights, we introduce Compact Attention, a hardware-aware sparse attention framework for efficient long-video synthesis. We quantify this stability and use it to construct searchable, reusable, head-specific masks. Based on the observation, we propose a deformable dual-window sparse pattern mechanism that flexibly approximates local and axial attention regions within one tile-aligned mask family. To avoid manually choosing a mask for each head, we develop a recall-driven offline auto-search algorithm that identifies high-quality sparse masks while leveraging temporal stability for mask reuse. Extensive evaluations on Wan2.1 (14B) and Hunyuan-Video demonstrate that Compact Attention achieves 2.25x/3.03x attention speedups over FlashAttention-2 and 1.20x/1.74x over FlashAttention-3, respectively, while maintaining visual quality and temporal consistency comparable to full-attention baselines. Project Page: https://yo-ava.github.io/Compact-Attention.github.io/

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03219

Can Coding Agents Sustain Continual Evolution without Forgetting?

Shangziqi Zhao, Yitong Zhang, Jia Li

原文摘要

Recently, harness self-evolution has emerged as an important paradigm for improving agent performance. Prior studies show that evolving the agent harness can effectively enhance agent capabilities without modifying the foundation model's parameters. We find that existing approaches largely study agent evolution on a single task. In practice, coding agents may need to handle multiple task types over time. Whether an agent harness can continuously evolve across multiple tasks without suffering from catastrophic forgetting remains an important open question. In this paper, we investigate this open question through a comprehensive empirical study of continual harness self-evolution. We organize three software engineering task datasets into six different evolution directions and apply two representative harness evolution methods to perform continual evolution along these directions. Our experimental results show that evolution on a subsequent dataset can substantially undermine the performance on previously seen datasets. For example, with DeepSeek-V4-Flash-0731, Meta-Harness improves its performance on SWE-Bench to 94.0% after evolution. However, after further evolution on FeatureBench, its performance on SWE-Bench drops to 32.0% by the fifth round, corresponding to a 66% relative decrease. Finally, based on our experimental findings, we identify several insights, providing directions for the continual self-evolution of agent harnesses. Code is available at https://github.com/THU-Agent/continual-harness-evolution.

cs.SE · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02478

MetaPOCS: Meta-Learned Multi-Exit Unfolding for Budget-Aware Seismic Data Interpolation

Baisong Jiang, Chunxia Zhang, Xiaoli Wei, Anxiang Di, Nanqi Zhao, Deng Xiong

原文摘要

Seismic data interpolation must reconcile reconstruction fidelity with the computational budgets of acquisition quality control and subsequent imaging. Fixed-depth unfolding networks are optimized for a single operating point, whereas their intermediate estimates may be insufficiently trained for early deployment. We present MetaPOCS, a meta-learned multi-exit unfolding framework for budget-aware seismic interpolation. Building on a spatial--spectral POCS reconstruction backbone, MetaPOCS supervises intermediate stages and preserves the acquired traces at every exit. A Meta-GF-based optimization procedure constructs exit-oriented parameter displacements from a common initialization and learns their fusion weights through an equal-exit outer objective. The weights vary across parameter tensors and respect the nested support of the reconstruction stages. We derive the resulting fusion hypergradient to characterize how the outer objective reallocates competing updates, and distinguish this mechanism from scalar loss reweighting. At inference, one checkpoint provides a set of reconstruction budgets through a latency-calibrated exit policy. The evaluation framework couples matched-cost interpolation comparisons with finite-update diagnostics and a fixed reverse-time-migration operator, connecting multi-exit optimization to waveform and imaging fidelity. MetaPOCS thus provides a unified formulation for studying the reconstruction--computation trade-off of seismic unfolding across deployment budgets.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v6arXiv:2601.21656

TabClustPFN: A Prior-data Fitted Network for Tabular Data Clustering

Tianqi Zhao, Guanyang Wang, Yan Shuo Tan, Qiong Zhang

原文摘要

Prior-data Fitted Networks (PFNs) have reframed supervised tabular learning as single-pass in-context inference without per-dataset optimization. Extending this paradigm to clustering is appealing yet fundamentally more challenging, due to absent supervision, unknown cluster cardinality, and label switching inherent to partition outputs. Existing PFN-based clustering methods address these challenges only partially, either requiring known cardinality as input or relying on unstable label-ordering conventions and overly restrictive synthetic priors. We introduce TabClustPFN, a clustering PFN that resolves all challenges jointly through co-designed prior, objective, and architecture. Our hybrid pretraining prior captures heterogeneous real-tabular geometry; our decoupled partition inference network--cardinality inference network architecture jointly infers cluster assignments and cardinality in a single pass; and our SoftARI training objective is permutation-invariant by construction, eliminating the need for any label-ordering convention. On a 44 curated real-world tabular benchmark, TabClustPFN achieves state-of-the-art clustering performance against classical, deep, and amortized baselines, with runtime comparable to efficient baselines. Code is available at https://github.com/Tianqi-Zhao/TabClustPFN.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37437

Representing the Unseen: Rethinking Visual Imagination for Spatial Reasoning

Zengqi Zhao, Zun Wang, Shoubin Yu, Mohit Bansal, Yue Zhang

原文摘要

Visual world models increasingly support spatial reasoning by generating RGB views of unobserved states. However, RGB has become the default interface largely because it is well suited for visual generation, rather than necessarily being the most effective representation for downstream reasoning. Different spatial questions may benefit from different representations of the same imagined state: top-down geometry can facilitate global distance comparisons, while panoramic views provide broader context for reasoning about open space. To isolate the role of representation in spatial imagination, we introduce ImagiRep, a simulator-grounded benchmark of 922 questions spanning seven question types across two settings, Viewpoint Imagination and Cross-view Integration, providing oracle RGB and six alternative representations (e.g., depth, panoramic views, and top-down geometry) of the same unobserved state. This controlled comparison reveals that even accurate imagined RGB does not guarantee accurate spatial reasoning, and different questions benefit from different representations. Similar trends hold for representations derived from world-model-estimated states. Notably, estimated RGB alone can mislead spatial reasoning, while alternative representations retain complementary benefits. Building on these findings, we introduce GEARS, a lightweight test-time strategy for adaptive representation selection, outperforming existing spatial imagination methods and showing that reasoning about unseen states requires more than generating RGB views.

cs.CV · cs.AI · cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.13008

Provenance-Aware Belief Updating in Human-AI Feedback Loops

Tianyi Huang, Eric Liu, Jay Chong, Emily Yin, Wanqi Zhao

原文摘要

In repeated human-AI interaction, a single observation can be quoted, summarized, and returned to a model as multiple reports, making repetition look like independent corroboration. We study whether language models can use supplied report lineage to update according to evidence provenance rather than report count. We introduce Latent Quotient Evidence Training (LQET), a relational objective that combines sparse Bayesian supervision with constraints that encourage stable predictions when reports add no new evidence and consistent updates to the same new evidence across relay histories. On a controlled binary benchmark, LQET reduces new-evidence update RMSE by 41-44\% and log-odds drift after eight relay steps by 89-91\% relative to task-only training across Qwen3-4B and Phi-4-mini, with three paired training seeds per backbone. The gains extend to a closed-loop simulation in which model confidence controls how many relayed reports re-enter the model's context in later rounds. By grounding belief updates in supplied provenance, LQET offers a route toward dynamic human-AI systems in which influence follows new evidence, not the number of times the same evidence returns.

cs.AI

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1093ACL Anthology · ACL 2026

LegalChainReasoner: Grounding Criminal Judicial Opinion Generation via Structured Legal Chains

Weizhe Shi, Qiqi Wang Nankai University, Yihong Pan University of Auckland, Qian Liu University of Auckland, Kaiqi Zhao Harbin Institute of Technology, Shenzhen

原文摘要

A criminal judicial opinion represents the judge’s disposition of a case, including the decision rationale and sentencing. Automatically generating such opinions can assist in analyzing sentencing consistency and provide judges with references to past similar cases. However, current research typically approaches this task by dividing it into two isolated subtasks: legal reasoning and sentencing prediction. This separation often leads to inconsistency between the reasoning and predictions, failing to meet real-world judicial requirements. Furthermore, prior studies rely on manually creating knowledge to enhance applicability, yet such methods remain limited in practical deployment. To address these limitations and better align with legal practice, we propose a new LegalAI task: Criminal Judicial Opinion Generation, which simultaneously produces both legal reasoning and sentencing decisions. To achieve this, we introduce LegalChainReasoner framework that applies structured legal chains to guide the model through comprehensive case assessments. By integrating factual premises, composite legal conditions, and sentencing conclusions, our approach ensures flexible knowledge injection and end-to-end opinion generation. Experiments on real-world, open-source Chinese legal case datasets demonstrate that our method outperforms baseline models.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1255ACL Anthology · ACL 2026

SAM3-I: Segment Anything with Instructions

Jingjing Li University of Alberta, Yue Feng Nanjing University of Aeronautics and Astronautics, Yuchen Guo, Jincai Huang, Wei Ji Yale University, Qi Bi Utrecht University, Yongri Piao Dalian University of Technology, Miao Zhang Dalian University of Technology 等 13 位作者

原文摘要

Segment Anything Model 3 (SAM3) advances open-vocabulary segmentation through promptable concept segmentation, enabling users to segment all instances associated with a given concept using short noun-phrase (NP) prompts. While effective for concept-level grounding, real-world interactions often involve far richer natural-language instructions that combine attributes, relations, actions, states, or implicit reasoning. Currently, SAM3 relies on external multi-modal agents to convert complex instructions into NPs and conducts iterative mask filtering, leading to coarse representations and limited instance specificity. In this work, we present SAM3-I, an instruction-following extension of the SAM family that unifies concept-level grounding and instruction-level reasoning within a single segmentation framework. Built upon SAM3, SAM3-I introduces an instruction-aware cascaded adaptation mechanism with dedicated alignment losses that progressively aligns expressive instruction semantics with SAM3’s vision-language representations, enabling direct interpretation of natural-language instructions while preserving its strong concept recall ability. To enable instruction-following learning, we introduce HMPL-Instruct, a large-scale instruction-centric dataset that systematically covers hierarchical instruction semantics and diverse target granularities. Experiments demonstrate that SAM3-I achieves appealing performance across referring and reasoning-based segmentation, showing that SAM3 can be effectively extended to follow complex natural-language instructions without sacrificing its original concept-driven strengths. Code and dataset are available at https://github.com/debby-0527/SAM3-I.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1451ACL Anthology · ACL 2026

Disentangling Reasoning Logic to Resolve Explicit Knowledge Conflicts

Xianda Zheng University of Auckland, Zijian Huang University of Auckland, Meng-Fen Chiang, Jiamou Liu The University of Auckland, Yuan Fang Singapore Management University, Michael J. Witbrock University of Auckland, Kaiqi Zhao Harbin Institute of Technology, Shenzhen

原文摘要

Explicit knowledge conflicts, where retrieved contexts contain contradictory information, have become increasingly prevalent as Large Language Models (LLMs) integrate diverse data sources. The core challenge lies in the complexity of entangled narratives and the heterogeneity of conflict cases, which impose excessive demands on the reasoning capabilities of standard models. To address this, we propose Knowledge Conflict Reasoning (KCR), a framework that adjudicates conflicts by structuring the underlying logic. KCR first disentangles conflicting contexts into distinct sets of reasoning traces, utilizing both textual and graph-based representations, to simplify comprehension. It then employs a Reinforcement Learning with Verifiable Rewards (RLVR) paradigm, guiding the model to internalize a reasoning process that maximizes logical consistency while actively suppressing spurious reasoning paths derived from contradictory contexts. Extensive experiments demonstrate that KCR yields substantial improvements: a KCR-enhanced 7B model surpasses the performance of baselines equipped with top-tier closed-source models such as GPT-4o and GPT-5.1.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1469ACL Anthology · ACL 2026

Evo-PI: Aligning Medical Reasoning via Evolving Principle-Guided Supervision

Xianda Zheng University of Auckland, Huan Gao, Meng-Fen Chiang, Michael J. Witbrock University of Auckland, Kaiqi Zhao Harbin Institute of Technology, Shenzhen, Shangyang Li Beijing University of Post and Telecommunications

原文摘要

Despite recent progress, the reasoning capabilities of large multimodal language models (MLLMs) remain fundamentally constrained by static supervision, where fixed prompts, rules, or reward models provide non-adaptive guidance throughout training. Such static signals are often sufficient to enforce output formats, but fail to shape the underlying reasoning process, leading to brittle generalization and performance saturation in complex decision-making tasks. We propose Evo-PI, a principle-centric learning framework that treats reasoning principles as explicit, language-based supervision signals that can be generated, evaluated, and iteratively evolved. Instead of relying on fixed rewards, Evo-PI enables a co-evolutionary loop in which principles guide model reasoning, while model behaviors in turn refine the principles that supervise them. This dynamic alignment mechanism allows supervision to progressively adapt to the model’s reasoning deficiencies. We instantiate Evo-PI in medical visual question answering as a high-stakes testbed requiring structured visual–textual reasoning. Across eight benchmarks and multiple model backbones, Evo-PI consistently improves reasoning accuracy, achieving gains of up to 24.6%. Our results suggest that evolving principle-guided supervision offers a scalable and general paradigm for training expert-aligned reasoning in multimodal language models.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1857ACL Anthology · ACL 2026

Measuring Social Bias in Vision-Language Models with Face-Only Counterfactuals from Real Photos

Haodong Chen, Qiang Huang Harbin Institute of Technology (Shenzhen), Jiaqi Zhao, Qiuping Jiang Ningbo University, Xiaojun Chang, Jun Yu Harbin Institute of Technology

原文摘要

Vision-Language Models (VLMs) are increasingly deployed in socially consequential settings, raising concerns about social bias driven by demographic cues. A central challenge in measuring such social bias is attribution under visual confounding: real-world images entangle race and gender with correlated factors such as background and clothing, obscuring attribution. We propose a face-only counterfactual evaluation paradigm that isolates demographic effects while preserving real-image realism. Starting from real photographs, we generate counterfactual variants by editing only facial attributes related to race and gender, keeping all other visual factors fixed. Based on this paradigm, we construct FOCUS, a dataset of 480 scene-matched counterfactual images across six occupations and ten demographic groups, and propose REFLECT, a benchmark comprising three decision-oriented tasks: two-alternative forced choice, multiple-choice socioeconomic inference, and numeric salary recommendation. Experiments on five state-of-the-art VLMs reveal that demographic disparities persist under strict visual control and vary substantially across task formulations. These findings underscore the necessity of controlled, counterfactual audits and highlight task design as a critical factor in evaluating social bias in multimodal models.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.2216ACL Anthology · ACL 2026

Reinforced Efficient Reasoning via Semantically Diverse Exploration

Ziqi Zhao, Zhaochun Ren Leiden University, Jiahong Zou, Liu Yang, Zhiwei Xu Shandong University, Xuri Ge Shandong University, Zhumin Chen Shandong University, Xinyu Ma Baidu 等 12 位作者

原文摘要

Reinforcement learning with verifiable rewards (RLVR) has proven effective in enhancing the reasoning of large language models (LLMs). Monte Carlo Tree Search (MCTS)-based extensions improve upon vanilla RLVR (e.g., GRPO) by providing tree-based reasoning rollouts that enable fine-grained and segment-level credit assignment. However, existing methods still suffer from limited exploration diversity and inefficient reasoning. To address the above challenges, we propose reinforced efficient reasoning via semantically diverse explorations, i.e., ROSE, for LLMs. To encourage more diverse reasoning exploration, our method incorporates a semantic-entropy-based branching strategy and an \varepsilon-exploration mechanism. The former operates on already sampled reasoning rollouts to capture semantic uncertainty and select branching points with high semantic divergence to generate new successive reasoning paths, whereas the latter stochastically initiates reasoning rollouts from the root, preventing the search process from becoming overly local. To improve efficiency, we design a length-aware segment-level advantage estimator that rewards concise and correct reasoning while penalizing unnecessarily long reasoning chains. Extensive experiments on various mathematical reasoning benchmarks with Qwen and Llama models validate the effectiveness and efficiency of ROSE. Codes are available at https://github.com/ZiqiZhao1/ROSE-rl.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表