Artificial Intelligence · Cross-listed from cs.CV
AdaThinkV: Adaptive Thinking for Token-Efficient Video Reasoning
Jingqi Tian, Haoji Zhang, Lin Chen et al. (+7 more)
Chain-of-thought (CoT)
reasoning can improve performance on difficult
video questions but often wastes decoding
tokens on simple ones. We study whether a
video multimodal large
language model can adapt its
reasoning effort to each question. We propose AdaThinkV, an adaptive framework for
video reasoning that learns whether to reason explicitly without offline difficulty labels, manually tuned confidence thresholds, or an external router. During reinforcement learning, AdaThinkV samples matched rollouts in explicit
reasoning and direct answering modes for each
prompt. ThinkGain estimates the
prompt-level utility of explicit
reasoning by balancing its accuracy gain against additional response length, providing supervision for both conditional response generation and autonomous mode selection. For difficult
prompts, limited rollout exploration can yield groups in which every response is unsuccessful and accuracy rewards show little variation, providing insufficient signal for learning. We therefore introduce Variance Recovery Policy Optimization (VRPO), which retains and progressively expands these groups to recover informative signals from
prompts that are difficult yet solvable. At inference, AdaThinkV selects a response mode and generates the response in a single autoregressive sequence. Across a unified suite of
video reasoning evaluations, AdaThinkV achieves a mean accuracy of 40.79 with an average of 257.20 output
tokens, outperforming the strongest evaluated adaptive baseline by 2.98 points while using 22.7% fewer
tokens. Project page: https://trilarflagz.github.io/AdaThinkV/
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All authors Jingqi Tian, Haoji Zhang, Lin Chen, Hongbo Jin, Haonan Xu, Tianrui Zhu, Xingming Shui, Shilin Ma, Wenjing Yang, Yansong Tang
Artificial Intelligence · Cross-listed from eess.AS
Music Restoration via Latent Operator Optimization and Diffusion Model Priors
Michal Švento, Eloi Moliner, Valtteri Kallinen et al. (+3 more)
Music restoration seeks to recover a clean signal from an observed recording degraded by an unknown effect, distortion, or corruption. Existing systems often rely on paired training data and distortion-specific supervision, which limits their use when the forward process is not known in advance. We propose LOUDAR (Latent-space Optimization of Unknown Distortion for Audio Restoration) a general-purpose restoration method that operates in the latent space of a pretrained audio autoencoder and models the unknown distortion as a learnable latent operator. At inference time, LOUDAR alternates between estimating the clean latent variable and updating the latent operator parameters. An unconditional latent
diffusion model provides a prior over clean audio and regularizes this inference by steering the latent estimate toward the
manifold of clean recordings. Because the degradation model is adapted per input, the approach is broadly applicable across diverse restoration problems. We evaluate LOUDAR on singing voice effect removal and restoration, as well as guitar distortion removal, and show that it consistently improves over degraded inputs and is competitive with supervised and unsupervised baselines in waveform and latent domains.
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All authors Michal Švento, Eloi Moliner, Valtteri Kallinen, Lauri Juvela, Vesa Välimäki, Pavel Rajmic
Artificial Intelligence · Cross-listed from cs.CV
FAST-GS: Frequency Aware Space-time Gaussian Splatting for Photorealistic Dynamic Novel View Synthesis
Zhengyang Zhang, Ziyu Lu, PengCheng Li et al. (+6 more)
4D Gaussian Splatting (4DGS) excels in dynamic 3D reconstruction and real-time novel view synthesis via efficient 4D Gaussian representations and parallelizable rendering. However, existing 4DGS approaches rely on a single
polynomial to model motion, which limits performance in complex dynamic scenes where high-frequency motion components are prevalent, and fails to ensure long-term stability due to cumulative trajectory drift. To address these issues, we propose a
Fourier Motion Modeling module: this paradigm decomposes motion into frequency-based sinusoidal components, capturing both low-frequency global trajectories and high-frequency local details to model complex motion patterns accurately. It retains the real-time rendering capability of 4DGS while improving complex motion fitting and long-term coherence. Additionally, we integrate a motion-aware regularization strategy into the loss function: it uses frequency-dependent weights to suppress high-frequency jitter while preserving low-frequency motion coherence. Extensive experiments on N3V and Google Immersive
datasets from multiple scenarios demonstrate the effectiveness of our method.
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All authors Zhengyang Zhang, Ziyu Lu, PengCheng Li, Hongbo Duan, Yi Liu, Pengting Luo, Peiyu Zhuang, Xinghui Li, Shaohua Ma
Artificial Intelligence · Cross-listed from cs.ET
Agentic Self-Healing for Data and AI Pipelines: An Affordable Vendor-Agnostic Architecture using Open-Source Software
Solomon Eshun, Dennis Murage, Sharleen Muoki et al. (+4 more)
Modern organizations rely on data, machine learning, and software delivery pipelines to move data, train models, deploy applications, refresh dashboards, and support business-critical decisions. However, these pipelines often fail because of data quality issues, schema changes, upstream source changes, infrastructure problems, orchestration failures, and model workflow issues. Existing ZeroOps, observability, and AI operations platforms can help teams detect incidents, investigate root causes, and in some cases recommend or execute fixes. However, many of these solutions are expensive, vendor-specific, or difficult for smaller teams to adapt across different tools and environments. This paper first compares existing off-the-shelf solutions for AI-assisted pipeline monitoring, root-cause analysis, and automated remediation, including their strengths, limitations, and practical trade-offs. Based on this comparison, we find that the main gap is architectural rather than technological: the required ingredients for self-healing pipelines already exist, but they are fragmented across vendor-specific platforms, observability tools, incident systems, and open-source components. We therefore propose an affordable, vendor-agnostic reference architecture for agentic self-healing pipelines using open-source and low-cost tools. The proposed architecture combines monitoring, pipeline metadata, incident history, deterministic policy checks, AI-assisted diagnosis, approval workflows, and controlled remediation actions to help teams detect, diagnose, repair, verify, and learn from pipeline issues with less manual effort. The goal is to provide a practical reference architecture that can be adapted across data engineering, machine learning operations, and software delivery environments.
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All authors Solomon Eshun, Dennis Murage, Sharleen Muoki, Chih-Chun Chen, Stephen Adjignon, Matteo Staar, Oliver Angélil
Artificial Intelligence · Cross-listed from q-bio.NC
Divisive Normalization Shapes Low-Rank Slow Manifolds for Continuous Working Memory
Zhaotian Gu, Jie Su, Weiwei Wang et al. (+3 more)
The ability to robustly maintain and update continuous variables is a hallmark of working
memory. While classical continuous attractor networks suffer from severe fine-tuning fragility, standard artificial recurrent neural networks (RNNs) like GRUs and LSTMs typically fail to stably learn continuous
manifolds, instead shattering the state space into discretized point attractors. To bridge this gap, we draw inspiration from divisive normalization, a canonical neural computation widely observed across cortical
circuits, and propose the Recurrent Divisive Normalization Network (RDNN), a minimal and algebraically isolated model of dynamic division. Through dynamical systems analysis on canonical working
memory tasks, we demonstrate that this biophysical constraint allows the network to converge to robust, high-fidelity slow
manifolds. Furthermore, we analyze the gradient dynamics of divisive normalization during Backpropagation Through Time (BPTT), showing that it introduces an activity-dependent local gradient scaling. This scaling dampens parameter updates in highly active regimes, which empirically aligns with a significant self-compression of the network's effective rank, confining the recurrent dynamics to a tight, low-dimensional subspace while avoiding the optimization pathologies associated with explicit low-rank factorization. Finally, ablations demonstrate that while subtractive inhibition can maintain static
memories, divisive normalization is mathematically essential to prevent
manifold shattering under time-varying inputs. Our findings identify divisive normalization not merely as a biological artifact, but as a critical computational mechanism for learning high-fidelity continuous representations.
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All authors Zhaotian Gu, Jie Su, Weiwei Wang, Chang Liu, Tianyi Qian, Dahui Wang
Artificial Intelligence
A Contractualist Argumentation Framework for Moral Decision-Making
Luis Marcos-Vidal, Giulio Antonio Abbo and Tony Belpaeme
Autonomous
agents operating in shared environments must make decisions that affect multiple individuals with potentially conflicting interests. We propose a formal framework for moral decision-making grounded in Scanlon's contractualism, an ethical theory that evaluates the permissibility of actions in terms of principles that no one could reasonably reject. To operationalise contractualist
reasoning, we use ASPIC+, a structured argumentation framework, extended with value-based filtering to model how each
agent's values determine which reasons are morally relevant in the first place. The result is a Contractualist Argumentation Framework in which
agents' reasons are formally represented, compared, and evaluated through argumentation semantics. We illustrate the approach through a worked example in a domestic setting and discuss its relation to existing value-based argumentation approaches.
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Artificial Intelligence · Cross-listed from cs.SI
Semantic Networks as Clues: A Theoretical Foundation and Process Optimization for Semantic Network Construction
JinWoo Ha and Dongsoo Kim
The subject matter of this paper is twofold. One is to review the theoretical foundation of a specific type of Semantic Networks (SNs) representing textual non-propositional knowledge. The other involves proposing a framework (ClueNetwork) for ranking candidate SNs generated through various Semantic Network Construction (SNC) processes for the type. In the first fold, it is clarified that the type serves as clues, not surrogates, of reality, making gold standards elusive. Then, it is discussed why this type nevertheless holds scientific legitimacy in terms of abduction. Grounded in this legitimacy, the three main stages of SNC, comprising Automatic Keyphrase Extraction (AKE), Edge Weighting (EW), and Community Detection (CD), are reviewed alongside their objectives and operations. In the second fold, evaluation criteria (comprising two established and one reformulated) for achieving the objectives are first defined and justified, followed by illustrative experiments based on the criteria. Thereafter, SNC is reformulated as a Process Optimization Problem (POP), and its global objective function that integrates the local criteria is defined and justified. Based on these, ClueNetwork is ultimately proposed.
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Artificial Intelligence · Cross-listed from cs.CL
Automatic Annotation of Ancient Greek Vowel Length
Albin Thörn Cleland and Eric Cullhed
Prior work in Ancient Greek NLP relies on corpora that do not disambiguate the phonemic vowel length of alpha, iota, and ypsilon, together known as the dichrona. Depending on lexeme, morphology, sandhi, syntax, and conventions of period, genre, and verse form, each of these letters can represent either a long or a short vowel. Deciding and marking the correct length is known as "macronizing", a long-tail problem given the sheer mass of word forms and the context dependency of individual instances. No macronized corpus of Ancient Greek is publicly available at scale, so a stand-alone macronizer is needed. While previous work has shown how to build a static, corpus-bespoke vowel-length dictionary, the present paper constructs the first general-purpose macronizer for arbitrary Ancient Greek input. Given input carrying lemma, part-of-
speech, and morphological annotation in the standard CoNLL-U format, a set of recursive modules lets less common word forms inherit markup from more common forms of the same lexical word. The macronizer's chief application is generating training data for machine learning: we show that a small character-level
transformer trained on the macronizer's own output learns to generalize past the cases the rule-based system leaves unmarked, matching or exceeding its accuracy on a gold-standard, manually annotated
benchmark of verse and prose. We also show that macronization can improve downstream prosodical NLP tasks like verse scansion.
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Artificial Intelligence · Cross-listed from cs.CV
Recompute or Reuse? Diagnosing and Mitigating Textual Shortcuts in VLM Self-Reflection
Wenxiao Fan, Jingling Fu, Fang Li et al. (+9 more)
Vision-
language models (VLMs) are expected to revise their
reasoning when visual evidence changes. Failures to do so are often attributed to insufficient visual
attention or contextual inertia, leaving unclear what models reuse instead of recomputing from the current image. We show that evidence-bearing
reasoning in a prior chain of thought (CoT) can form a textual shortcut that competes behaviorally with visual recomputation. Across 16 VLMs, a matched counterfactual analysis identifies evidence-bearing content as the most robust carrier of prior-CoT influence. Removing this evidence-bearing content shifts answer preference more than removing length-matched non-evidence context or the final-answer span, with prior control weakening progressively as more stale evidence is removed. Reordering this evidence also weakens prior control, showing that its organization modulates shortcut strength. Beyond the immediate answer, the shortcut can retain residual influence after answer correction: weakening current-image support shifts preference back toward the prior answer, while repeated prior answers and reused premises arise mainly when the shortcut remains active. To limit this influence, we introduce Fresh-State
Attention Firewall (FSAF), a training-free intervention that isolates fresh computation from the prior CoT. Across five VLMs, FSAF raises visual update rate from 35.28% to 53.61% and reduces prior-answer rate from 39.22% to 3.67%. Reliable VLM self-reflection therefore requires more than looking again: fresh visual recomputation must be protected from stale textual reuse.
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All authors Wenxiao Fan, Jingling Fu, Fang Li, Luohang Liu, Yu He, Lichen Ma, Zhiyang Yu, Weishan Bi, Junshi Huang, Yan Li, Gu Simiu, Kan Li
Artificial Intelligence · Cross-listed from cs.SE
Effective and Efficient Context Retrieval via Partial Dependency Graph for Repository-Level Code Generation
Zhongxin Liu, Zhonghao Jiang, Zhifan Ye et al. (+3 more)
LLM-based repository-level code generation aims to generate code using the context available in a software repository, requiring LLMs to reason over complex code dependencies. Due to limited context windows and insufficient repository-specific understanding, LLMs typically rely on
retrieval-augmented generation (RAG) to incorporate relevant code. Early RAG approaches primarily employ similarity-based
retrieval, which often fails to retrieve code snippets that the target function depends on. Recent work introduces graph-based
retrieval to model such dependencies, but typically relies on manually designed rules and static global graphs, leading to limited flexibility and high construction and maintenance costs. In contrast, human developers collect helpful context by implicitly constructing a partial dependency graph and iteratively inspecting along it. Inspired by this behavior, we propose DyRetriever, an efficient context
retrieval method via partial dependency graphs. DyRetriever uses an LLM to first select a set of entry-point functions and then perform multi-hop
reasoning along the code dependency graph. During multi-hop
reasoning, it uses the LLM's semantic understanding to validate whether a function can help generate the target function, eliminating manually designed rules and enabling flexibility across scenarios. Instead of statically constructing a global dependency graph, DyRetriever builds a partial graph on demand and discards it after use, reducing construction and maintenance costs. We integrate DyRetriever with a similarity-based code retriever to build DyCoder and evaluate it on CoderEval and DevEval. Experimental results show that DyCoder achieves relative Pass@1 improvements of 25.63% and 59.73% on CoderEval and DevEval, respectively, compared with existing RAG-based methods, while being 7.4x faster than baselines based on static dependency graph construction.
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All authors Zhongxin Liu, Zhonghao Jiang, Zhifan Ye, Haoye Wang, Jiakun Liu, Xiaoxue Ren
Artificial Intelligence
ProWorld: Progress-Aware Hyperbolic World Models for Long-Horizon Visual Goal Reaching
Zihan Liu, Yuzhe Zhuang, Yuanzu Li et al. (+4 more)
JEPA-style visual world models offer an effective paradigm for visual goal planning by predicting future latent representations. Existing methods typically learn local transition consistency through next-step representation prediction. However, in long-horizon tasks, accurate local prediction alone need not ensure sustained progress toward the goal. First, multi-step rollouts can remain locally plausible while drifting away from goal-relevant trajectories. Second, locally similar future states can correspond to substantially different long-term progress, making them difficult to distinguish in a latent space optimized mainly for local consistency. To address these challenges, we introduce goal-conditioned progress order, a relative ordering of states according to how they advance toward a given goal. This order exhibits an asymmetric, coarse-to-fine structure: early states retain broader future possibilities, while later states concentrate on more specific goal-relevant regions. Such a structure is well suited to
hyperbolic geometry. Motivated by this observation, we propose ProWorld, a progress-aware
hyperbolic visual world model. ProWorld leverages goal-conditioned progress order to organize visual latent-space dynamics, maintains directional progress within trajectories via
hyperbolic entailment learning, and mitigates progress ambiguity among locally similar future states via
hyperbolic future discrimination. Furthermore, we design a progress-aware planning objective that scores candidate rollouts by jointly considering proximity to the goal and sustained progress across intermediate states. Experiments on four visual goal-reaching tasks demonstrate that ProWorld achieves an average absolute success-rate gain of 9.67 over LeWM. The code will be released after the paper is accepted.
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All authors Zihan Liu, Yuzhe Zhuang, Yuanzu Li, Wanshuang Gou, Jiahong Liu, Min Zhou, Menglin Yang
Artificial Intelligence · Cross-listed from cs.CE
TransNRank: Towards Accurate Neoantigen Ranking with Transformer
Zhiyin An, Yuenan Hou, Shumeng Duan et al. (+3 more)
Personalized neoantigen prediction is challenging due to the scarcity of positive samples, the noise of the experimental data, the severe class imbalance trait and the complex of immunogenicity features. Prior arts, such as linear regression and XGBoost fail to model long-range dependencies and contextual relationships within peptide features, therefore the performance of neoantigen positive recall rate is limited. In this paper, we present a novel deep learning framework based on
Transformer, coined as TransNRank. By leveraging the self-
attention mechanism, our model captures both local and global feature contexts, enabling more accurate recognition of immunogenic neoantigens. A positive-aware training objective is utilized to handle the class imbalance problem, assigning more weights to those few positive samples. Extensive experiments are performed on NCI, TESLA and HiTIDE
datasets. Notably, our TransNRank can push the upper bound top 20 recall rate of neoantigen prediction from 46.9% (45 from 96) to 53.1% (51 from 96), while reducing the training epochs from 200 epochs to 20 epochs. Furthermore, we analyze the features contribution based on TransNRank and find that the mutation at anchor and TCGA expression level play an unexpected important role in neoantigen prediction, and removing insignificant features to reduce the input dimensionality of peptides does not drastically impair the overall performance of the model. Our paradigm not only streamlines the prediction pipeline but also sets a new state-of-the-art for neoantigen discovery, with broad implications for accurate immuno-oncology.
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All authors Zhiyin An, Yuenan Hou, Shumeng Duan, Yiming Zhou, Yuanting Zheng, Leming Shi
Artificial Intelligence
Diagnosing Search Behavior and Failure Modes in Long-Horizon Search Agents
Qi Liu, Jiaxin Mao, Fengbin Zhu et al. (+1 more)
Deep search
agents answer difficult information-seeking questions by iteratively issuing search queries to gather supporting evidence, but it remains unclear whether and how greater search effort leads to better answers. We study these questions through a trajectory-level diagnosis of long-horizon search
agents. Using human-annotated document-level relevance judgments, we evaluate the evidence retrieved at each search step and separate two stages of
agent behavior: what evidence an
agent retrieves and how effectively it uses that evidence. This distinction further allows us to decompose failures into
retrieval gaps, where the necessary evidence is never found, and utilization gaps, where relevant evidence is retrieved but not used correctly. With the
retrieval model and evaluation harness held fixed, we compare six
agents on BrowseComp-Plus and further validate our findings on BrowseComp with an open-web search API. Across settings, we find that search effort and answer quality are only weakly aligned. Answer accuracy is better correlated with the quality of retrieved evidence, especially cumulative
retrieval recall, than with the number of searches or the amount of context consumed. Useful evidence often appears early in the trajectory, yet
agents tend to continue searching, producing a long tail of low-yield
retrieval steps. At the query level, exploratory reformulations remain useful, but the best-performing
agents issue far fewer redundant queries. Overall, by systematically characterizing the search behavior and failure modes of long-horizon search
agents, this work points to practical directions for building better deep research systems, including stronger query formulation, more effective evidence selection and context management, and stopping criteria based on whether sufficient supporting evidence has been retrieved.
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All authors Qi Liu, Jiaxin Mao, Fengbin Zhu, Tat-Seng Chua
Artificial Intelligence
CoEvoKG: Co-Evolving Knowledge Graphs with Self-Evolving Search Agents
Zhaoyang Li, Zenghuang Fu, Qiuyuan Ai et al. (+6 more)
Large
language models can improve with reinforcement learning for search
agents, yet existing self play
agents repeatedly generate tasks while discarding the knowledge gained during successful searches. We introduce CoEvoKG, a framework that turns a knowledge graph into both a source of verifiable training tasks and a persistent evidence
memory for
agent evolution. CoEvoKG jointly trains a task generator and a search
agent: the generator creates multihop questions from entity chains sampled from the knowledge graph, while the
agent learns from rewards for answer correctness and search trajectories whose entity paths are supported by graph evidence. When a search succeeds, CoEvoKG verifies and deduplicates the retrieved evidence, then writes it back to the corresponding graph nodes and edges. Future rounds reuse this enriched graph for task generation and reward computation, closing the loop between model self
evolution and knowledge accumulation. Experiments on six QA
benchmarks (NQ, TriviaQA, PopQA, HotpotQA, 2WikiMultiHopQA, and Bamboogle) with three backbone models show that CoEvoKG improves macro average accuracy over the corresponding base models by +11.2, +10.1, and +11.6 points on Qwen2.5-3B-Instruct, Qwen2.5-7B-Instruct, and Llama-3.1-8B-Instruct, respectively. Under matched training budgets, CoEvoKG further improves over competitive self play baselines and RL baselines for search
agents by +2.6 to +3.7 macro average points across the three backbones. Code is available at https://github.com/lazzy1225/CoEvoKG.
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All authors Zhaoyang Li, Zenghuang Fu, Qiuyuan Ai, Ping Jiang, Haoyu Wu, Minghui Wu, Chenxu Zhao, Jie Song, Guannan He
Artificial Intelligence · Cross-listed from cs.DC
Energy-Efficient LLM Serving via Disaggregated Attention–FFN and Flexible Frequency Scaling
Cunchen Hu, Liangliang Xu, Tian Liu et al. (+9 more)
Large
language model (LLM) serving spans diverse applications with stringent service-level objectives (SLOs), often requiring GPUs to run at maximum frequencies and increasing energy consumption. Existing energy-management approaches adapt GPU frequencies only at the request or inference-phase level, overlooking operator-level differences in frequency sensitivity between
Attention and feed-forward networks (FFNs). We find that the energy-optimal frequencies of
Attention and FFN (A/F) differ and vary with the inference phase, workload, and system configurations. However, runtime variability and independent A/F frequency control create a large search space and high communication overhead. To address these challenges, we present AFlex, a framework that jointly optimizes resource provisioning and GPU frequency scaling for disaggregated A/F serving. AFlex introduces a global scheduler and a local operator-level dynamic voltage and frequency scaling (DVFS) controller to determine A/F resource allocations and frequencies. It further introduces an interleaved A/F pipeline with dynamic microbatch depth and adaptive request batching to reduce pipeline bubbles. We implement AFlex in SGLang and evaluate it on NVIDIA A800 GPUs using Qwen3-32B and Mixtral-8$\times$7B under production Conversation and Coding traces. \AFlex reduces energy per
token by up to 49% over state-of-the-art disaggregated serving and 48% over frequency-scaling systems while satisfying TTFT and TPOT SLOs.
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All authors Cunchen Hu, Liangliang Xu, Tian Liu, Min Lyu, Yongkun Li, Sa Wang, Shuo Quan, Yanan Yang, Wenda Tang, Yiduo Wang, Fu Yu, Jie Wu
Artificial Intelligence · Cross-listed from cs.MM
Hear, Invoke, and Understand: A Skill-Calling Multimodal Agent for Large Audio Language Models
Yuwen Wang, Tian-Hao Zhang, Minghao Cai et al. (+7 more)
Complex acoustic problems may require models to perform acoustic operations, interact with external tools and reason over the resulting textual or processed-audio observations rather than answer directly from a fixed audio input. We study such problems as tool-interactive audio
reasoning and develop SpeechAgent-R, an audio
agent that coordinates its intrinsic
multimodal understanding with external skills and tools. To support this capability, we construct HIU-Corpus, comprising 65,492 interaction trajectories and 507.6 hours of audio across 24 tasks, 8 skills and 9 tools. SpeechAgent-R first learns structured interaction behaviors through trajectory-based supervised fine-tuning and then improves its decisions through multi-turn reinforcement learning. We further introduce HIU-Bench to jointly evaluate task performance, interaction quality and generalization to diverse task settings. It contains 1,395 samples across 56 tasks, including in-distribution (ID) and out-of-distribution (OOD) splits with substantial shifts in tool usage and workflow composition. SpeechAgent-R achieves 84.17 on ID tasks and 70.94 on OOD tasks, improving over the base model under the same
agent harness by 15.40 and 14.23 points. These results demonstrate that learning skill and tool coordination improves audio
agents' ability to handle diverse task settings and adaptive tool interactions.
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All authors Yuwen Wang, Tian-Hao Zhang, Minghao Cai, Yilin Ren, Ziyang Jiang, Xin Wang, Zhichao Wang, Pan Zhou, Kun Zhan, Xinyuan Qian
Artificial Intelligence · Cross-listed from cs.LG
LAB-Tab: LLM-Augmented Bayesian Network Adaptation for Few-Shot Tabular Generation
Zijian Shen, Taijie Chen, Bin Zhou et al. (+2 more)
Tabular data generation supports analysis and decision-making when target-domain data are scarce, yet collecting complete target samples is often costly. A practical but underexplored setting provides only a few target records together with richer source data from a related domain. Existing few-shot tabular generators often either fit sparse target statistics directly, which can overfit incidental patterns, or reuse source-domain generators, which may preserve dependencies that no longer hold in the target domain. To address this problem, we propose LAB-Tab, an LLM-augmented
Bayesian network (BN) adaptation framework for source-aware few-shot tabular generation. LAB-Tab first fits a BN from source data and then uses an LLM to propose plausible target-domain BN edges that are absent from the source BN graph. This step converts semantic and weak statistical evidence into explicit structural hypotheses, thereby expanding the editable edge space beyond the source-fitted graph. Because the proposed edges may be noisy and interact with existing dependencies, a PPO policy calibrates edges in the augmented BN through edge-level actions, including keep, weaken, strengthen, flip, and deactivate. The PPO policy is trained with a reward that combines distributional
alignment, downstream utility, and preservation of target-relevant dependencies. The adapted BN is then sampled to synthesize target-domain tables. Across six source–target distribution-shift scenarios built from three US Census (ACS) prediction tasks, LAB-Tab achieves the best performance at the 10% target-data budget, leads four of the six individual scenarios, and reduces the macro Overall score by 33.8% relative to the strongest baseline. It also obtains the best macro JSD, WAPE, and UtilityGap while maintaining competitive feature–label preservation.
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All authors Zijian Shen, Taijie Chen, Bin Zhou, Ziyang Jiang, Jintao Ke
Artificial Intelligence
ReasonCast: Towards Explainable Time Series Forecasting with Reasoning
Seunghan Lee, Jun Seo, Jaehoon Lee et al. (+9 more)
Most time series (TS) models are specialized for a single task, either understanding (i.e., returning text answers about a TS) or generation (i.e., returning a numeric forecast). Only recently have unified models begun to handle the two within a single architecture. Even these models, however, produce the two outputs as task-separated paths and cannot predict a series and explain why that prediction arises within a single coherent response. In this paper, we argue for a task-fused model that jointly produces 1) prediction (generation) and 2) selfexplanation (understanding), thereby integrating 1) numerical TS
forecasting and 2) interpretable text
reasoning within a single response. To enable the systematic study of this capability, we present both a
benchmark and a recipe that jointly address the two tasks. The
benchmark, ReasonTS-Bench, identifies five fundamental patterns underlying TS and enables the joint evaluation of both tasks. ReasonCast, our recipe for finetuning any LLM to perform both tasks jointly, yields a model that generates a
reasoning chain and a forecast together in a single autoregressive pass. Extensive experiments show that ReasonCast outperforms both LLMs and TS models on prediction accuracy while producing verifiable, causal
reasoning. Code is available at: https://github.com/seunghan96/reasoncast.
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All authors Seunghan Lee, Jun Seo, Jaehoon Lee, Junhyeok Kang, Sangjun Han, Sungdong Yoo, Minjae Kim, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, Soonyoung Lee, Wonbin Ahn
Artificial Intelligence · Cross-listed from cs.CY
No One Wins in Nuclear War: A Social Simulation of Military Decision-making
Glenn Matlin, Isaac Song, Anthony Wen-Ming Zang et al. (+1 more)
WOPR is a social-simulation environment for studying how organizations make high-stakes decisions, built on a deterministic, replay-validated rules engine and using wargames as the vehicle. We instantiate it first with the published card game
Nuclear War, traced against its published rules. We start with military decision-making because of its safety implications and because it needs further study, but the design is not specific to it: the decision-point contract that exposes the engine to
agents is reusable across verifiable rule systems. Existing social-simulation work emphasizes persona fidelity and synthetic opinion, but lacks a verifiable rules engine with replay-checkable mechanics and private-channel negotiation. WOPR supplies that engine, and its contract makes every strategic choice an explicit
agent decision. The method is agnostic to social-simulation frameworks; we adopt Concordia as the default harness for driving the game. On the same engine, WOPR layers a four-rung press ladder from silence to private single-recipient channels with structured commitments, and instantiates each faction as a collective command-and-control system rather than a single
agent. We make all code, example configurations, and replay data publicly available at https://github.com/eilab-gt/wopr.
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All authors Glenn Matlin, Isaac Song, Anthony Wen-Ming Zang, Mark Riedl
Artificial Intelligence
Wnuan: Staged Post-Training for Question Answering over Proprietary Enterprise Knowledge
Xiaofeng Shi, Xiaosong Qiu, Wenxin Ma et al. (+6 more)
Enterprise question answering requires models to acquire proprietary knowledge without discarding general capabilities. We present Wnuan, a three-stage pipeline that constructs task-oriented supervision from documents, performs supervised fine-tuning with general-data replay, and applies reinforcement learning to residual errors. On the 707-question WnuanBench, the primary 32B route raises acceptable-answer rate (AAR) from 52.76% before adaptation to 80.06% after SFT and 91.51% after RL. Under a matched 100-update protocol, residual-error sampling outperforms full-pool and size-matched random sampling by 3.11 and 2.97 points, respectively. Source-cluster bootstrap intervals remain above zero for both contrasts, and a same-domain validation set preserves the ordering. The general-
benchmark average decreases by 5.17 points across the route, concentrated in instruction following. The automatic evaluation ensemble agrees with an authoritative domain expert on 90.5% of a stratified Wnuan-Inst response sample. These results characterize both the gains and the general-capability cost of staged enterprise adaptation.
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All authors Xiaofeng Shi, Xiaosong Qiu, Wenxin Ma, Qian Kou, Yiming Pan, Longbin Yu, Ying Liu, Haiping Wang, Hua Zhou
Artificial Intelligence · Cross-listed from cs.LG
Beyond Magnitude and Shape: A Direction-Aware Loss for Time Series Forecasting
Seunghan Lee, Jaehoon Lee, Jun Seo et al. (+9 more)
The direction of change — whether a series will move up or down — is often as important as its exact value in decisiondriven applications such as risk management and financial
forecasting. However, most
forecasting losses optimize either point magnitude or shape and frequency structure, and none explicitly targets the direction of change. In this paper, we find that MSE-trained forecasters fail on the direction of small moves. To address this, we propose CosDir, a simple yet effective direction-aware loss that aligns the difference vectors of the prediction and the target via cosine similarity. Being scale-invariant, CosDir keeps a directional gradient on small moves, re-injecting learning signal exactly where MSE neglects it. CosDir is a lightweight, plug-in term that attaches to any backbone without architectural modification. Since the best ratio for mixing the directional and magnitude terms differs across
datasets, we further propose CosDir-UW, an extension that makes this ratio adaptive by learning it during training, matching a per-
dataset tuned weight with no hyperparameter. We conduct over 100K experiments, demonstrating that our method consistently and significantly improves directional accuracy while preserving magnitude accuracy, and that it outperforms various loss functions. Code is available at: https://github.com/seunghan96/cosdir.
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All authors Seunghan Lee, Jaehoon Lee, Jun Seo, Junhyeok Kang, Sangjun Han, Sungdong Yoo, Minjae Kim, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, Soonyoung Lee, Wonbin Ahn
Artificial Intelligence
EchoChange: A Diffusion Language Model with Dual Pass Remasking for Factual Remote Sensing Disaster Change Captioning
Dongwei Sun, Bowen Yao, Yujie Zhang et al. (+3 more)
Bi-temporal remote-sensing disaster change captioning often needs to identify sparse and spatially localized changes across large pre- and post-event scenes and then translate them into coherent, factual descriptions. However, existing change captioning methods always follow an autoregressive decoding paradigm to generate the change description and thus an early misinterpretation of the changed object, event, or spatial relation becomes an irreversible premise for subsequent text, amplifying visual ambiguity into cascading factual errors. To address this limitation, we propose EchoChange, a
multimodal discrete
diffusion language model that formulates change captioning as iterative masked-
token denoising rather than left-to-right generation. By repeatedly revising the entire caption while conditioning on the image pair, EchoChange can reconsider uncertain content and correct imperfect intermediate predictions. We further introduce draft-aware dual-pass training, a progressive masking curriculum, and confidence-guided remasking to align training with iterative inference. Extensive experiments on the RSCC
benchmark show that EchoChange substantially outperforms both general-purpose and remote-sensing-specific baselines across lexical and semantic metrics. The EchoChange Project is at https://sundongwei.github.io/EchoChange_Project/
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All authors Dongwei Sun, Bowen Yao, Yujie Zhang, Pei Liu, Jing Yao, Xiangyong Cao
Artificial Intelligence · Cross-listed from cs.RO
Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills
Gaytri Jena, Kapil Wanaskar, Vinija Jain et al. (+3 more)
Robot learning is splitting into two bets: policies that bake competence into frozen weights (
vision-
language-action, or VLA, models), and
agents that write and refine their own executable skills as code. This survey organises the field around that axis of weights versus skills. Its central analytical contribution is a deep-dive that arranges code-as-policy methods by their degree of self-improvement, from zero-shot program synthesis, through closed-loop self-repair and persistent skill
memory, to the sparsely populated
cell in which execution feedback, skill
memory, and evolutionary search combine into one open-ended loop; only a few very recent systems (for example ASPIRE, ENPIRE, and RoboClaw) occupy that
cell. We map the complementary "skills" pole, from unsupervised reinforcement-learning skill discovery to large-
language-model skill libraries, and show that the word "skill" is used in at least five distinct senses, of which only the code sense self-improves without gradient updates. We then connect the taxonomy to the emerging skill economy: commercial
robot-skill marketplaces now distribute one-tap skills across
robots but ship only static playback, which surfaces open problems of adaptation, cross-embodiment portability, provenance, safety verification, composition, and standardisation. This is a deliberately focused survey. Rather than cataloguing the field exhaustively, it examines 77 representative systems across six technique families through one taxonomy and a set of contrast tables, and it supplies operational definitions of the self-improvement mechanisms together with a statement of what each family cannot do.
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All authors Gaytri Jena, Kapil Wanaskar, Vinija Jain, Aman Chadha, Vasu Sharma, Amitava Das
Artificial Intelligence
Physics-Informed Neural Networks for Complex Eigenfrequency Identification and Mode Structure Reconstruction of the Ground-State ITG Branch
Dengdi Sun, Bingbing Zhang, Xiao Wang et al. (+5 more)
Physics-informed neural networks (PINNs) combine sparse observations with physical equations, providing an important approach for modeling complex
plasma processes and inferring unknown physical quantities. The steep-gradient pedestal of high-confinement-mode tokamaks is closely linked to
plasma confinement and edge transport. Analyzing ion-temperature-gradient (ITG) drift waves in this region requires jointly identifying complex eigenfrequencies and reconstructing two-dimensional complex-valued mode fields. Localized high-frequency oscillations, strong real-imaginary coupling, and nonlinear coupling between the mode field and eigenfrequency challenge PINN representation and joint optimization. To address these challenges, we propose a physics-informed neural framework combining
Fourier feature encoding, complex-valued feature propagation, and three-stage training. Under sparse observations and physical constraints, it jointly solves for the complex eigenfrequency and mode field of a representative ground-state ITG branch. Experiments show that the framework accurately recovers the target complex eigenfrequency and two-dimensional complex-valued mode field and outperforms representative PINN baselines. It also provides a basis for analyzing higher-order and multiple-branch drift-wave modes.
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All authors Dengdi Sun, Bingbing Zhang, Xiao Wang, Zikang Yan, Yuqiang Tao, Qingquan Yang, Guosheng Xu, Jin Tang
Artificial Intelligence
Exploring and Bridging Knowledge Holes in Unlearned Multimodal Large Language Models
Junxiang You, Junkai Chen, Yuhao He et al. (+3 more)
Machine unlearning offers a promising approach to remove unsafe content from
Multimodal Large
Language Models (MLLMs), yet ensuring the precision of unlearning remains a persistent challenge. One reason is that current MLLM unlearning evaluation paradigms suffer from a critical blind spot: they assess model utility through
benchmarks whose representations are distant from the forget set, failing to capture knowledge holes—severe degradation on benign adjacent inputs. To probe knowledge holes in unlearned MLLMs, we construct a
benchmark that captures unintended degradation on benign inputs sharing generic patterns with the forget set, and confirm through controlled experiments that they are a systematic consequence of commonly used approaches. Furthermore, to bridge this gap, we propose Selective Protection with Anchored Regularization, which protects generic patterns via anchored activation filtering while reinforcing them through entity-abstracted enhancement. Our experiments on SafeEraser demonstrate that SPAR recovers over 98% of vanilla response quality compared to below 50% for standard baselines—while achieving 0.00% attack success rate and competitive model utility. These results underscore the necessity of more fine-grained evaluation for trustworthy MLLM unlearning.
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All authors Junxiang You, Junkai Chen, Yuhao He, Ruiqi Liu, Zhetao Guo, Shu Wu
Artificial Intelligence
FOCUS: FP4 Optimization via Coupled-Relaxation and Dual-Granularity Scaling
Xianglong Yan, Hong Liu, Chengzhu Bao et al. (+4 more)
Large
language models (LLMs) achieve remarkable performance but are expensive to deploy due to their enormous size. FP4 quantization, with formats such as MXFP4 and NVFP4, offers an appealing solution with native hardware support on modern accelerators. However, maintaining accuracy under FP4 precision remains difficult. A key bottleneck lies in scale optimization: existing methods tightly couple the quantization and dequantization scales, forcing both to conform to the discrete low-precision format required by hardware, such as E8M0 in MXFP4. Yet the quantization scale is never stored and need not obey this constraint, suggesting a significant untapped optimization space. In this work, we propose FOCUS, a post-training quantization framework with end-to-end scale learning for FP4 Optimization via Coupled-Relaxation and Dual-Granularity Scaling. Coupled-Relaxation Scaling (CRS) relaxes the tight coupling between quantization and dequantization scales with a learnable full-precision coefficient, enabling more effective optimization without breaking hardware compliance. Dual-Granularity Scaling (DGS) further refines the quantization scale at a finer sub-block granularity, allowing more precise adaptation to local weight distributions. Experiments across multiple LLM families and
benchmarks show that FOCUS achieves state-of-the-art FP4 accuracy under both MXFP4 and NVFP4 formats, while introducing no additional inference overhead. Code and quantized models will be released at https://github.com/tencent/AngelSlim.
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All authors Xianglong Yan, Hong Liu, Chengzhu Bao, Tianao Zhang, Guanghua Yu, Jianchen Zhu, Yulun Zhang
Artificial Intelligence
PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning
Chunji Lv, Yangguang Wei, Junlin Liu et al. (+6 more)
Large
language model
agents have shown strong potential in complex interactive tasks, yet their reinforcement learning (RL) is often hindered by sparse rewards, as a long multi-turn trajectory may receive only a single outcome-level signal. On-policy self-
distillation (OPSD) provides dense
token-level supervision from a privileged teacher, but the teacher may not be reliable at every position. Existing methods commonly rely on isolated
token-level discrepancies, which can be sensitive to noise, or assign a shared step-level weight that may overlook positional variation. We propose Persistent Consistency Self-
Distillation (PCSD), which derives
token-level
distillation weights from the local persistence of teacher-favoring signals. PCSD combines adaptive windows with exponentially decayed aggregation to capture persistent relative teacher support, applies trend-aware modulation to attenuate locally declining support, and produces continuous weights through sigmoid gating. The resulting objective is jointly optimized with GRPO, combining dense teacher guidance with sparse environmental feedback. Without inference-time skills, PCSD achieves the best ALFWorld Overall results among all baselines on both backbones, exceeding GRPO by 15.6 and 13.3 points and SDAR by 6.2 and 5.5 points, while remaining competitive on WebShop and gaining 15.8 points over GRPO on unseen ALFWorld split.
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All authors Chunji Lv, Yangguang Wei, Junlin Liu, Yang Gao, Ming Liu, Xinming Wang, Jinyang Wu, Guoren Wang, Changsheng Li
Artificial Intelligence
Rewriting or Reweighting? A Geometric Account in Language Models
Juntong Wang, Shengkun Yang, Xiyuan Wang et al. (+1 more)
Post-training can substantially alter
language-model behavior, yet aggregate behavior rates do not reveal whether training removes an existing mechanism, creates a new one, or changes how an inherited mechanism is used. We study this question through two mechanistically distinct failures, repetition as a decoding-attractor pathology and sycophancy as a preference-related
alignment failure. We introduce behavioral
manifold analysis, which isolates behavior-specific geometry by selecting sparse behavior-associated coordinates and lifting them into low-dimensional local charts. We construct these charts in two complementary spaces. ACT captures runtime activation states, while NOC quantifies how strongly the model routes functional information flow through the shared behavior-associated subspace. Across multiple model families, the resulting charts are highly compressed and partially alignable across architectures. Contribution-space charts expose a more architecture-robust shared core, whereas activation-space charts retain stronger family-specific structure. Tracking these charts through controlled post-training reveals a consistent asymmetry. Supervised fine-tuning substantially alters the inherited behavioral geometry, whereas reward optimization changes behavior while largely preserving the underlying chart. This
geometric perspective provides a unified framework for understanding the mechanistic distinction between the two objectives. SFT tends to rewrite behavioral geometry, whereas reward optimization primarily reweights it. Code is available at https://github.com/ronglingze/
Manifold-Analysis
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All authors Juntong Wang, Shengkun Yang, Xiyuan Wang, Muhan Zhang
Artificial Intelligence · Cross-listed from cs.CV
DeepVoyager-VL: Incentivizing Vision-in-the-Loop Search for Long-Horizon Multimodal Agents
Huanyao Zhang, Jiepeng Zhou, Runhao Zhao et al. (+12 more)
Multimodal large
language models (MLLMs) have advanced visual understanding and
reasoning, yet their static parametric knowledge limits their ability to address knowledge-intensive and dynamically evolving open-world problems. To move beyond this limitation,
multimodal deep search has emerged as a key direction for open-world information access, evolving from single-turn factual
retrieval toward long-horizon, multi-turn search guided by visual evidence. However, existing methods typically confine
vision to the input or answer stage, overlooking its role in intermediate
reasoning, and lack designs tailored to long-horizon interaction. Consequently, visual evidence rarely drives continued
retrieval, constraining both interaction depth and
reasoning span. To address these limitations, we propose DeepVoyager-VL, a long-horizon
multimodal deep-search framework for
vision-in-the-loop search. Specifically, we construct a
multimodal event graph to drive data synthesis, yielding problems with intermediate visual dependencies and long
reasoning chains. We then design an
agent framework for active visual acquisition and on-demand image loading. Finally, we fine-tune models on the synthesized data without reinforcement learning. Extensive experiments across ten
multimodal search
benchmarks demonstrate the effectiveness of our method.
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All authors Huanyao Zhang, Jiepeng Zhou, Runhao Zhao, Yanzhe Shan, Jiaoyang Chen, Bowen Zhou, Bo Li, Fang Wang, Jialong Wu, Zhengwei Tao, Lang Mei, Xiaohan Yu, Liyan Liu, Chong Chen, Wentao Zhang
Artificial Intelligence · Cross-listed from cs.CV
PartMat: Material-Aware 3D Part Decomposition with a Single Global Latent
Guangming Fu, Jin Song, Yiyun Fei et al. (+3 more)
Part-level 3D generation has recently attracted increasing
attention for producing structured and editable 3D assets. However, existing methods typically decompose objects according to functional semantics rather than the editable material boundaries (e.g., fabric, wood, metal) required in practical 3D applications such as interior design. Additionally, current methods often generate parts independently, causing computational costs to scale linearly with the part count. To address these limitations, we present PartMat, an efficient material-aware 3D part decomposition pipeline that represents multi-part geometry with a single global latent. Given a reference image and a single whole-object geometry, PartMat decomposes the object into parts that follow material boundaries. First, we propose PartVAE to learn such a unified representation and decode all material parts in a single forward pass, thereby decoupling inference cost from the number of parts. Second, with this representation, a
diffusion model is trained for part generation and refined via reinforcement learning for accurate material assignment and overlap suppression. Finally, to recover fine-grained
geometric details, we introduce a sparse-voxel flow-matching model with part
attention for geometry post-processing. Extensive experiments demonstrate that PartMat significantly outperforms existing baselines in material-aware decomposition accuracy and achieves comparable
geometric quality, while maintaining efficient inference.
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All authors Guangming Fu, Jin Song, Yiyun Fei, Guoqiu Li, Ruigao Yang, Jianan Jiang