Artificial Intelligence · Cross-listed from cs.CL
Jianguo Huang, Lipeng Wan, Yanchen Deng et al. (+1 more)
Diffusion large
language models (dLLMs) generate text by iterative unmasking. At each denoising step, a dLLM proposes a
token at every masked position, but the decoder commits only a confident subset of these proposals. Trace-based on-policy
distillation (TOPD) builds on this process by matching the student to a stronger teacher, yet only at the committed positions. We argue that this discards much of the useful signal, which resides in the uncommitted proposals, where the student has made a prediction but is not yet confident enough to commit it. We call these proposals hesitations. In our pilot study on an SDAR-4B student, hesitations make up only 24% of supervisable state-position pairs but carry 66% of the teacher-student divergence. To exploit this signal, we propose Hesitation-Aware On-Policy
Distillation (HOPD), which extends teacher distribution matching to every masked position of each denoising step. Because hesitations are not equally informative, we further allocate supervision using hindsight from the completed trajectory, placing more weight on positions whose proposal was later disagreed with the final
token and on blocks where first-step proposals rarely survive. Since both models already produce distributions at all masked positions, HOPD requires no additional forward passes over TOPD. The only extra cost is evaluating the loss at more positions. With SDAR-1.7B and SDAR-4B students distilled from TraDo-8B-Instruct, HOPD achieves the best average score among the evaluated methods on five math and coding
benchmarks, under both static and dynamic decoding and at both scales. It also speeds up decoding. On SDAR-4B, the HOPD student hesitates less and commits 11% more
tokens per denoising step than TOPD, while reaching higher accuracy.
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All authors Jianguo Huang, Lipeng Wan, Yanchen Deng, Bo An
Artificial Intelligence · Cross-listed from cs.RO
AquaWAM: A Dynamics-aware World Action Model for Underwater Embodied Agents
Cunhao Zhu, Yifeng Wang, Dongliang Xu et al. (+3 more)
World Action Models (WAMs) are becoming increasingly important and useful for
embodied intelligence, as they enable
robots to anticipate the consequences of candidate actions before interacting with the physical environment. However,
underwater robots are usually subject to passive dynamics, such as inertia, buoyancy, hydrodynamic drag, and persistent drift, which can continue to affect the vehicle even after an action is completed. Existing WAMs, which primarily predict action-conditioned visual observations, are not explicitly designed to capture such passive motion dynamics. In this paper, we present AquaWAM, the first World Action Model designed for
underwater embodied agents. Instead of predicting future images, AquaWAM models both action-conditioned and passive physical dynamics, including the thruster dead band, the inertial glide that outlasts each command, and ambient currents. Specifically, it senses through the DVL, IMU, pressure sensor and joint encoders, while cameras supply only semantics for understanding goals and target pose. By modeling compact
navigation states rather than high-dimensional visual observations, AquaWAM substantially reduces the model size and computational cost compared with conventional WAMs. Experimentally, AquaWAM achieves a 72.6% task success rate across 20
underwater tasks on the USIM
benchmark, outperforming existing methods while making action decisions 2.7x faster than U0 on an NVIDIA Jetson AGX Orin. Our model also remains effective when some onboard sensor measurements are unavailable. For example, without DVL velocity measurements, our method still achieves a 61.6% success rate, compared with 39.4% for U0.
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All authors Cunhao Zhu, Yifeng Wang, Dongliang Xu, Yunzhong Hou, Yue Yao, Chi Harold Liu
Artificial Intelligence
The Error You See Is Not the Error You Made: Progression-aware Reasoning Origin for Reasoning Error Localization
Yiguo Wang, Ziyuan Yang, Yi Zou et al. (+3 more)
Verifying multi-step LLM
reasoning requires more than determining whether a trace is correct: a useful verifier should identify where the
reasoning first goes wrong. However, existing holistic methods provide little positional evidence, while forward sequential verification often treats the first rejected step as the error source. Under error propagation, this assumption can fail, since an earlier mistake may remain locally plausible and become observable only through its downstream consequences. We therefore rethink
reasoning verification as a progression-aware error-source localization problem: rather than asking only where a
reasoning trace first appears inconsistent, we ask which earlier step best explains how that inconsistency emerges along the trajectory. Based on this view, we propose Progression-aware
Reasoning Origin (PRO), a training-free framework for first-error localization. PRO jointly models incoming support from the preceding context and outgoing compatibility with subsequent
reasoning, selectively refines regions where these signals disagree, and finally performs detector-conditioned source attribution with intervention-based evidence to distinguish the true error origin from its propagated manifestations. We further formalize the gap between forward rejection and structural exposure, showing why incoming-side evidence alone is insufficient for reliable localization under error propagation. Experiments across open-form, medical, and structured
reasoning tasks demonstrate consistent improvements over strong verification baselines, supporting progression-aware source attribution as a more faithful formulation of
reasoning verification.
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All authors Yiguo Wang, Ziyuan Yang, Yi Zou, Dan Lin, Rongsheng Li, Yi Zhang
Artificial Intelligence
TraceDance: An Automated System for Building Agent Behavior Benchmarks from Real-World Agent Deployment Traces
Dehai Min, Daoan Zhang, Yiming Zeng et al. (+13 more)
An
agent can complete a task while exhibiting undesirable behavior during execution. Developers need tests for the specific behaviors encountered in deployment, beyond fixed
benchmark suites. We present TraceDance, an
agent system that constructs targeted
benchmarks from deployment traces for user-specified undesirable behaviors. For efficient construction, Anchor-and-Confirm combines programmable
retrieval with candidate-level confirmation by a Flash large
language model (LLM), while the Anchor Synthesis Loop generates and revises specifications for custom behaviors. The
benchmarks use decision-point continuation to evaluate an LLM's next turn at a recorded decision point with a behavior-specific rubric, without a reference answer or environment replay. Experiments in coding and general tool use draw on 252,557 sessions and produce 107
benchmarks with 4,125 instances, fulfilling 95.3% of build-target requests. Both human annotators confirm the requested behavior in 84% of sampled instances, and the automated grader's agreement with human pass/fail judgments is comparable to that between the annotators. Nine frontier LLMs achieve a mean pass rate of only 26.7%, showing that they still struggle to respond appropriately at the evaluated decision points. Analysis across behavior-specific
benchmarks further reveals weaknesses in how current LLMs behave as
agents. By turning deployment problems into targeted
benchmarks, TraceDance could serve as a key component of the recursive self-improvement (RSI) loop.
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All authors Dehai Min, Daoan Zhang, Yiming Zeng, Huayi Zhang, Ziyi Chen, Yan Zhang, Qinbo Bai, Mengyuan Chao, Jing Ning, Qiyue Hua, Huiyi Chen, Hanrong Zhang, Henry Peng Zou, Jie Yang, Wei Xu, Philip S. Yu
Artificial Intelligence
Learning to Sell: Reinforcement Learning for Strategic Large Language Model Agents in Multi-Product Markets
Shuze Daniel Liu, Claire Chen, Jiuqi Wang et al. (+2 more)
Autonomous large
language model (LLM)
agents operating in multi-product
markets must make sequential decisions under information asymmetry and resource constraints. We develop a machine learning approach for training such
agents to act effectively as sellers in a multi-item bargaining environment, where a seller concurrently negotiates a catalog of substitutable assets across a pool of independent buyers. Buyers hold private, heterogeneous valuations across products, and each can purchase at most one item. Facing limits on total communication turns, the seller must dynamically match buyers with the most profitable products considering their private valuations, while strategically allocating its limited interaction budget toward combinations of greater potential value. We formalize this problem as a Partially Observable Markov Decision Process using a structured, four-part message protocol that maps natural
language into a parsable and regulated decision space. Using this formalization, we design a post-training method using Reinforcement Learning from Verifiable Rewards (RLVR). To evaluate this framework, we construct a multidimensional metric suite that quantifies constraint adherence, seller surplus extraction, and allocation quality. Our trained seller
agent learns to match limited inventory to buyers more effectively, matching or outperforming trillion-parameter frontier models in both seller surplus extraction and buyer-product allocation quality. Finally, these learned strategies generalize robustly to unseen
market structures, correlated valuation distributions, and price ranges not encountered during training.
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All authors Shuze Daniel Liu, Claire Chen, Jiuqi Wang, David Simchi-Levi, Thorsten Joachims
Artificial Intelligence
Feedback Makes Perfect: A Closed-Loop Framework for NL-to-STL Translation
Bowen Ye and Xiang Yin
Signal Temporal Logic (STL) enables rigorous verification and control of cyber-physical systems, but writing correct specifications requires expertise that most requirement holders lack. Large
language models can translate natural-
language (NL) requirements into STL, yet stronger translators alone approach an accuracy ceiling. We argue that this ceiling stems from how the task is posed: one-shot, open-loop translation is somewhat ill-defined. Natural
language is ambiguous, and, more fundamentally, what a person writes may not always be what they intend, so the target specification is not fully contained in the input text. We therefore reformulate NL-to-STL translation as a closed-loop feedback process. Each generated formula is translated back into natural
language for the user to check, and natural-
language corrections drive revision until the user accepts the specification. Users never read or write formal syntax. This framework rests on an asymmetry familiar from feedback control theory. The forward path, from ambiguous
language to formal logic, is hard and error-prone. The feedback path, from structured STL back to
language, can be made highly precise, and a precise feedback path lets an imprecise forward path achieve precise closed-loop behavior. Experiments on 500 expert-authored requirements and seven LLMs support this view. Back-translated explanations agree with expert judgments in 99.5% of cases. Closed-loop refinement raises strong models from about 89% open-loop accuracy to 98.0–99.2%, and yields gains of over 30 percentage points for weaker models (e.g., 17.6%$\rightarrow$48.0%). Ablations show these gains come from the semantic content of the feedback rather than from repeated attempts. An expert audit and a 280-session user study further confirm the reliability of the loop. We also identify a capability threshold above which feedback no longer helps.
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Artificial Intelligence
RINI: Seeing the Prior Is Not Enough
Hongyi Du, Tianyi Zhang, Heng Wang et al. (+7 more)
A research proposal can describe an established mechanism correctly while claiming to introduce it. We study whether providing the earlier paper corrects such contribution claims. Three controlled experiments compare proposals generated with a contribution-bearing prior and a same-topic control. Providing the prior yields no clear aggregate reduction in unsupported novelty. Human analysis of 175 interpretable exposed proposals finds that 137 recognize the prior's relevance, but 61 correctly attribute the established contribution. Of 71 proposed remaining distinctions, 37 are covered by the same prior. We introduce Research Idea Novelty Inspection (RINI), which audits contribution claims against evidence, checks the remaining distinction, and applies local revisions. Five human annotators evaluate 1,080 original-revision pairs across three methods. On the same 240 originals judged to require correction, successful repair is 11.7% for Self-Revision, 39.1% for Retrieve-and-Revise, and 72.2% for RINI, with research tasks weighted equally. The improvement over same-evidence direct revision is 33.0 percentage points. The revised proposals retain their research questions and technical methods. These results motivate explicit contribution attribution when using literature to generate and revise research proposals.
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All authors Hongyi Du, Tianyi Zhang, Heng Wang, Zhelun Gao, Yimei Liu, Ambrose Luo, Annie Hao, Jiayan Ni, Jiawei Han, Jiaxuan You
Artificial Intelligence
Multi-Dimensional Comparative Scale Construction for Efficient Personalized Subjective Judgment in High-Traffic Applications
Xianglong Shi, Shifeng Liu, Sirui Zhao et al. (+2 more)
Subjective judgments are central to many high-traffic applications, but subjective intensity is difficult to quantify and perceptions vary substantially across individuals. To address these challenges, we propose a pairwise comparative framework for multi-dimensional scale construction. By comparing case-person pairs along case and profile dimensions, the framework constructs relative scales that capture both fine-grained intensity and individual variation. To support practical high-traffic deployment, we optimize both offline scale construction and online inference. For scale construction, we combine sparse Elo comparisons with multi-judge voting, cutting the comparison cost from $O(N^2)$ to $O(NK)$ for $N$ objects and a budget of $K$ opponents per object, while limiting reliance on any single judge. For inference, we propose SubJudge, a System One model for personalized scoring with Batchwise Preference Optimization (BPO). Using Bradley-Terry comparisons, BPO trains the model to learn relative orderings, and SubJudge reads a continuous score from digit-
token probabilities at the first response position, requiring only one forward pass per criterion and reducing the inference complexity to $O(1)$. Experiments on PluriHarms and iNews show that our 9B models match or surpass the evaluated frontier LLMs on multiple metrics. On the H100 GPU, SubJudge achieves an approximately $1.29\times$ to $261\times$ speedup in mean inference latency over Qwen3.5-9B with different thinking budgets. The code is available at https://github.com/Longchentong/SubJudge.
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All authors Xianglong Shi, Shifeng Liu, Sirui Zhao, Shengming Yuan, Enhong Chen
Artificial Intelligence · Cross-listed from cs.LG
Domain Generalization under Sampling Pattern Shifts in Irregular Time Series
Changhun Kim, Joohyung Lee, Kwanhyung Lee et al. (+3 more)
Irregularly sampled multivariate time series (ISMTS) are prevalent in real-world applications, where both observation times and available measurements can vary substantially across domains. While recent models increasingly exploit such sampling information for prediction, its robustness under sampling pattern shifts remains underexplored. We introduce HAR-C, to the best of our knowledge the first controlled
benchmark for sampling pattern shifts in ISMTS, and show that sampling shifts alone can substantially degrade performance, induce sampling-specific shortcuts, and remain challenging for existing domain generalization (DG) methods. Motivated by these findings, we propose PRISM, a DG framework that first learns complementary feature-centric and sampling-centric representations without task labels, and subsequently performs robust supervised training across diverse sampling variations to discourage brittle shortcut reliance. Extensive experiments on controlled and real-world ISMTS
benchmarks demonstrate that PRISM consistently improves robustness to unseen sampling shifts over existing methods. Our code is available at https://anonymous.4open.science/r/PRISM.
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All authors Changhun Kim, Joohyung Lee, Kwanhyung Lee, Donghwee Yoon, Grigorios Chrysos, Eunho Yang
Artificial Intelligence
ChronoFlow: Hierarchical Flow Matching for Irregular Time Series Generation
Changhun Kim, Sunguk Jang, Jeongjun Lee et al. (+5 more)
Recent advances in generative modeling have substantially improved time series generation, yet most existing methods either assume a regular temporal grid or focus on feature dynamics under a given sampling structure. This makes them illsuited for generating irregular time series in their native form, where a model must capture not only feature values, but also how many observations occur, when they occur, and which features are observed together. To address this heterogeneous generation problem, we propose ChronoFlow, a unified hierarchical flow matching framework organized by statistical granularity. Following a coarse-to-fine hierarchy, ChronoFlow first generates observation counts and feature-wise frequencies, then jointly generates observation times and feature co-observation patterns, and finally generates values conditioned on the realized pattern. This turns a complex joint generation problem into structurally aligned subproblems while preserving their dependencies. To evaluate complete irregular time series generation, we introduce complementary metrics spanning sample realism, sampling structure, value fidelity, and temporal and cross-feature dependencies, and validate them through controlled corruptions. Across five
benchmarks, ChronoFlow achieves strong improvements in generation fidelity over existing baselines, while factorization studies support the proposed hierarchy. Our code is available at https://anonymous.4open.science/r/ChronoFlow.
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All authors Changhun Kim, Sunguk Jang, Jeongjun Lee, Juhwan Choi, Sangchul Hahn, Grigorios Chrysos, Eunho Yang, Juho Lee
Artificial Intelligence
Next Thoughts Are Distributions: Generative Autoregressive Reasoning in the Latent Space
Yang Li, Yi Wang, Shiyuan Huang et al. (+3 more)
Reasoning problems often admit multiple valid ways to proceed. Continuous
reasoning promises to move computation beyond
language tokens into a more compact latent space, but representing several plausible ways to think next remains difficult. We introduce Autoregressive Thought Flow (ATF), which models the next continuous thought as a
multimodal distribution. A causal autoregressive model performs the
reasoning computation, while a lightweight
diffusion head generates a plausible next thought from the resulting condition. The sampled thought is fed back into the model, allowing continuous
reasoning to unfold for a variable number of steps while preserving the pretrained backbone. Across mathematical
reasoning tasks, ATF improves accuracy with compact latent traces and benefits from reinforcement learning and additional test-time thinking. Multi-sample evaluation shows broader solution coverage, indicating that its
multimodal predictions capture useful diversity among
reasoning paths. Our results suggest that continuous
reasoning is more effective when multiple possible next thoughts remain available rather than being collapsed into a single prediction.
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All authors Yang Li, Yi Wang, Shiyuan Huang, Yang Liu, Hao Wang, Chengzhi Mao
Artificial Intelligence
Structured Sparse Memory for Recurrent Reasoning
Zixuan Zhao, Samuel Wheeler, Neil Getty et al. (+3 more)
Recurrent models trained from scratch have recently become competitive on ARC-style
reasoning tasks, but the usual framing around small recurrent backbones overlooks two important parts of the system: task-conditioned
memory and synthetic augmentation data. We study this regime through CHARM, a compact hybrid ARC model that combines recurrent
reasoning with structured task
memory, synthetic data, and inference-time aggregation. In existing approaches, task-conditioned
memory supplies a large hidden source of capacity, reaching more than 30x the size of the recurrent backbone. We introduce a compositional sparse
embedding (CoSE) for task conditioning that reduces learned task-
memory parameters by over 90% while improving pass@2 in controlled ARC ablations. For the recurrent backbone, recurrent depth helps only when balanced with learning horizon. Combining these ingredients, our system reaches 84% pass@2 on ARC-AGI-1 and 46.7% pass@2 on ARC-AGI-2 public evaluation. The benefits of structured
memory also generalize to unseen puzzles and other domains. Our code,
dataset, and model checkpoints are available at https://github.com/water-vapor/charm.
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All authors Zixuan Zhao, Samuel Wheeler, Neil Getty, Xiaotian Duan, Rick Stevens, Fangfang Xia
Artificial Intelligence
LSTMem: Hierarchical Long Short-Term Online Memory for Large Language Models
Xianglong Shi, Ruijie Yang, Sirui Zhao et al. (+4 more)
Large
language models increasingly serve as long-horizon assistants and
agents, where they must both accumulate information across interactions and make the relevant parts available when later requests depend on them. Existing compact online
memories typically use a single persistent state both to accumulate history and to serve readout, so what the
memory stores cannot be controlled separately from what it exposes to the current computation. We propose LSTMem, an LSTM-inspired online
memory that instead equips each layer of a frozen LLM with two
matrix-valued states: a
cell state that accumulates history and a hidden state whose readouts correct the backbone's
attention. Input and forget gates control what the
cell stores, while an output gate separately controls what the
cell exposes through the hidden state. LSTMem further connects
memory across depth through forward hidden-state propagation and block-end feedback, and uses higher-layer reconstruction gradients to refine lower-layer
cell states before rebuilding hidden states from shallow to deep layers. Across
memory benchmarks on Qwen3-4B-Instruct, LSTMem consistently improves MemoryAgentBench, LoCoMo, and HotpotQA over the plain backbone. Comparisons further show that the LSTM-based
memory formulation outperforms an associative-
memory counterpart, while removing cross-layer hidden-
memory propagation degrades performance. These results demonstrate the benefits of separating
memory accumulation from
memory expression and organizing
memory hierarchically across model depth. The code is available at https://github.com/Longchentong/LSTMem.
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All authors Xianglong Shi, Ruijie Yang, Sirui Zhao, Shukang Yin, Zihao Bian, Tinghao Yi, Enhong Chen
Artificial Intelligence · Cross-listed from cs.SD
SCISSOR: Score-Conditioned Instrument Source Separation for Orchestral Recordings
Yiheng Lu and Hao-Wen Dong
Orchestral separation recovers instrument sections from mixtures in which shared pitches, harmonics, and timbres obscure source identity. An aligned score provides instrument labels, note pitches, and activity times. A score-informed approach appends piano rolls to audio features before mask prediction. We introduce SCISSOR (Score-Conditioned Instrument Source Separation for Orchestral Recordings), which uses the score to form a frame-wise query for each source. Each query matches a shared audio representation, and a softmax over instrument and background slots jointly assigns overlapping time-frequency evidence. The queries retain instrument identity even when notes are missing from the score. After training on SynthSOD and a small set of URMP and PHENICX-Anechoic recordings, SCISSOR achieves the highest average SDR on held-out real recordings. With SynthSOD-only training, it leads on SynthSOD and zero-shot PHENICX-Anechoic, and improves on its audio-only control on zero-shot URMP. SCISSOR also degrades less under score corruption than the evaluated score-based baselines.
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Artificial Intelligence · Cross-listed from cs.CV
VehDyn: A Driving World Model Benchmark for Vehicle Dynamics
Tianyi Wang, Wangsheng Du, Jiazhou Chen et al. (+12 more)
Video world models are emerging as data engines, action planners, and generative simulators for autonomous driving, but existing
benchmarks primarily assess visual fidelity and coarse physical plausibility, providing limited evidence on whether generated driving futures obey realistic vehicle kinematics and dynamics. This limitation is further compounded by the lack of
datasets in which vehicle, road, maneuver, and speed conditions are independently controlled, and ground-truth vehicle states are recorded in synchrony with
videos. We introduce VehDyn, a driving world model
benchmark for vehicle dynamics. VehDyn is built on a CARLA-CarSim co-simulation platform where photorealistic rendering is coupled with a validated multi-body dynamics model, and it contains 10,080 configurations from a full factorial design over five vehicle types, four tire-road friction coefficients, three maneuvers, four target speeds, 14 scenes, and three illuminations, each paired with synchronized position, velocity, and attitude sequences. Built on this
dataset, VehDyn introduces a hierarchical evaluation framework that measures trajectory
alignment, kinematic consistency, and dynamic consistency, and
benchmarks 12 state-of-the-art
video world models. We further assess the
video quality using two established protocols and correlate it with the VehDyn score. Trajectory-level metrics are nearly saturated, with ten of twelve models within 20% of ground truth, while no model reaches 92% of ground truth on dynamic consistency, and visual-quality metrics are only weakly correlated with vehicle-dynamics fidelity. DrivingWorld achieves the highest VehDyn score, followed by Cosmos 3 Nano and LTX-
Video 2.5, and the VehDyn score agrees closely with human judgment. VehDyn provides a systematic foundation for developing driving world models that are physically consistent and visually realistic.
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All authors Tianyi Wang, Wangsheng Du, Jiazhou Chen, Tianyi Zeng, Xiangyu Li, Jiseop Byeon, Yujin Wang, Yiming Xu, Yangyang Wang, Bingzhao Gao, Sikai Chen, Zhaomiao Guo, Junfeng Jiao, Christian Claudel, Alexandre Bayen
Artificial Intelligence
CORTEX: A Verified Experience Layer for Generalist Agents
Garapati Keerthana and Manik Gupta
An
agent can solve a task today and face the same task under new facts, tools, or governing knowledge tomorrow. Most
agent systems can retrieve relevant text or recall prior conversations, but they lack a principled way to decide when a previous solution is still valid, when it must be adapted, and when it should be discarded. We introduce CORTEX (Contextual Orchestration and Reuse of Task EXperience), a general AI systems framework that connects specialized
agents through an external layer of verified experience. Each episode records its task conditions, source and tool state, decisive predicates, proof trace, verifier, and outcome. A meta-controller chooses exact replay, checked adaptation, fresh synthesis, or escalation. Accepted episodes can become task patterns and procedural strategies through a challenge-driven development loop. This gives the system an implicit competence layer that can grow without changing model weights. We formalize system contracts for exact replay and source-version separation, and derive when reuse saves computation. A controlled two-domain implementation tests the exact-replay core on 1,000 synthetic cases. Complete-family holdouts test procedural transfer on 1,000 new-family cases across eight clinical and policy splits, with complete fresh-evidence grounding and perfect invariance to irrelevant-field and insertion-order perturbations. The transfer trace exposes the work required for verified strategy execution. These results establish an initial path toward general intelligence through reusable procedures, typed experience, and developmental transfer.
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Artificial Intelligence · Cross-listed from cs.LG
BERT4DTI : BERT-based Model for Predicting Drug-Protein Interactions
Thanina Hamitouch, Khadidja Henni, Abdelkrim Arie et al. (+3 more)
Understanding how drugs interact with
protein targets is fundamental to drug discovery, drug repurposing and the early identification of promising therapeutic candidates before costly experimental testing. Sequence-based DTI models face three practical limitations: labelled interactions are scarce and unevenly distributed, large pretrained chemical and
protein encoders are expensive to fine-tune end-to-end, and independently encoded sequences do not capture pair-specific dependencies. We present BERT4DTI, which encodes SMILES strings with ChemBERTa and amino-acid sequences with ProtBERT, applies bidirectional mutual
attention between
token-level representations, and classifies the resulting interaction features using convolutional layers and a multilayer perceptron. To reduce trainable size, ProtBERT is truncated to 18 retained layers and only the last two layers of each encoder are fine-tuned. On BIOSNAP, DAVIS and BindingDB, BERT4DTI is competitive, achieving the best ROC-AUC and PR-AUC on BIOSNAP and the highest sensitivity on all three
benchmarks. An ablation on DAVIS shows that mutual
attention improves PR-AUC and specificity. With 125M trainable parameters compared with 353M for full BERT fine-tuning, BERT4DTI provides a favourable performance-parameter trade-off for sequence-based DTI screening, while leaving runtime profiling, calibration and leakage-audited validation for future work.
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All authors Thanina Hamitouch, Khadidja Henni, Abdelkrim Arie, Amina Selma Haichour, Neila Mezghani, Lina Abou-Abbas
Artificial Intelligence · Cross-listed from cs.CV
VGGT-Diff: Visual Geometry Meets Diffusion for Sparse-View Novel View Synthesis
Kangjie Chen, Xiangyu Li, Dongbin Zhang et al. (+12 more)
We present VGGT-Diff, a geometry-routed multi-view
diffusion model for sparse-view novel view synthesis. Existing novel view synthesis (NVS) methods face a fundamental trade-off: reconstruction-based approaches preserve observed geometry but struggle to synthesize unseen regions, while
diffusion-based methods provide strong generative priors yet rely on implicit source-to-query correspondence. VGGT-Diff bridges these regimes by routing visual geometry latents from VGGT-{\Omega} into a pretrained
video diffusion model. Each visual
token is associated with a 3D point and confidence, then transformed into query-aligned latent conditions through a confidence-aware Visual Geometry Router (VGR) that preserves front and back surface evidence. These conditions guide joint target-view denoising, while Point-Track Residual Consistency (PTRC) regularizes predicted-clean residuals along reliable 3D tracks, improving multi-view stability. We further introduce robust geometry conditioning, combining training-time regularization with inference-time guidance for improved robustness. Experiments show competitive or state-of-the-art performance across interpolation and extrapolation under different viewpoint difficulties. Our code is available at https://github.com/chenkangjie1123/VGGT-Diff.
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All authors Kangjie Chen, Xiangyu Li, Dongbin Zhang, Chaoda Zheng, Shijia Chen, Jinhao Deng, Hongbin Lin, Choo Sin Wai, Minqi Wang, Minghao Yang, Dake Zhong, Guorui Song, Yu Zhang, Xianming Liu, Boyang Wang
Artificial Intelligence
ActiveMem: Dynamic Latent Memory Trees for Long-Horizon Agents
Song-Li Wu, Jingyi Wang, Zhaocheng Du et al. (+1 more)
Large
Language Model (LLM)
agents increasingly rely on external
memory to support long-horizon
reasoning and decision making. Existing
memory systems typically retrieve historical trajectories or summaries as independent context fragments, overlooking the procedural dependencies underlying multi-step execution. As
memory scales, such flat
retrieval introduces context fragmentation and cross-task interference, leading to structurally inconsistent
reasoning trajectories. We propose ActiveMem, a hierarchical
memory framework that recursively organizes
agent experiences into dependency-aware latent execution trees. ActiveMem abstracts trajectories into reusable subtask nodes while explicitly preserving execution transitions, enabling coherent
reasoning-path
retrieval conditioned on the current execution state. To support continual adaptation, ActiveMem further learns dynamic
memory expansion,
retrieval, and
pruning policies through reinforcement learning. Experiments across various
agent benchmarks demonstrate that ActiveMem consistently improves task completion,
reasoning stability, and
memory efficiency over existing
memory-based
agents. Moreover, ActiveMem enables compact open-weight models to achieve competitive performance with substantially larger proprietary systems.
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All authors Song-Li Wu, Jingyi Wang, Zhaocheng Du, Weinan Gan
Artificial Intelligence
CodeSkill: Latent Skill Abstraction for Long-Horizon Code Agents
Song-Li Wu, Jingyi Wang, Zhaocheng Du et al. (+2 more)
Code
agents require long-horizon decision-making over complex interaction trajectories. However, existing reinforcement learning (RL) approaches typically optimize behavior at the
token level, creating a mismatch between low-level generation and high-level behavioral
reasoning. This limitation leads to inefficient exploration and weak credit assignment under sparse rewards. Moreover, while large-scale
agent trajectories often contain recurring multi-step behavioral patterns, their noisy
token-level representations hinder effective experience reuse. To address these challenges, we propose CodeSkill, a framework that adapts hierarchical latent skill modeling to the code
agent domain. CodeSkill first leverages a teacher model to distill both successful and failed trajectories into multi-level textual abstractions. It then integrates temporal variational inference with reinforcement learning to map these discrete semantics into continuous latent variables, while an adaptive boundary mechanism dynamically gates skill transitions based on execution feedback. The learned skills are injected into a frozen LLM policy as latent semantic prefixes, enabling optimization in a compact semantic space rather than over raw
token sequences. By shifting RL from
token-level exploration to experience-level
reasoning, CodeSkill improves optimization efficiency and long-horizon behavioral coherence. Extensive experiments demonstrate that CodeSkill achieves highly competitive performance against strong open-weight baselines across diverse general and industrial coding
benchmarks. Furthermore, the learned skills exhibit strong transferability and robust cross-domain generalization, highlighting the effectiveness of explicit behavioral abstraction for scalable agentic code generation.
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All authors Song-Li Wu, Jingyi Wang, Zhaocheng Du, Weinan Gan, Weiwen Liu
Artificial Intelligence · Cross-listed from cs.IR
Inspire: Benchmarking Scientific Literature Search for Open Research Problems
Jianrong Ding, Zhengyan Shi, Jianyuan Zhong et al. (+5 more)
Scientific literature search often begins with an open research problem rather than a known target paper or a fixed candidate set. We introduce INSPIRE, a
benchmark for evaluating
agents that search prior literature to make progress on solution-redacted research problems. Each instance pairs a research brief with a target-specific cutoff three months before a later paper and evaluates ranked outputs against graded cited antecedents from that paper's realized research lineage. Search proceeds over an open corpus, while the identity of the target paper and membership of its cited antecedents remain hidden from the
agent. Beyond end-to-end
retrieval quality, INSPIRE uses logged search trajectories to distinguish three coupled stages: resource exposure, whether useful antecedents are surfaced during search; selection, whether exposed antecedents are retained; and ranking, how effectively retained papers are ordered. Across 476 computer-science targets under a shared search interface and budget, the strongest evaluated
agent achieves 0.284 nDCG@10. Results show that current
agents more readily recover an isolated antecedent than assemble a broader portfolio of relevant prior work. The stagewise analysis identifies resource exposure as the largest observed bottleneck, with further losses in selection and ranking. We additionally construct replay-valid hindsight demonstrations and show that they improve held-out search without changing test-time information, establishing that the
benchmark provides an actionable learning signal. INSPIRE therefore enables both end-to-end comparison and stage-resolved diagnosis in a setting where the
agent must construct its own working criterion of relevance.
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All authors Jianrong Ding, Zhengyan Shi, Jianyuan Zhong, Kai Qiu, Qi Dai, Yifan Yang, Chong Luo, Qiang Xu
Artificial Intelligence · Cross-listed from cs.LG
When Do Models Admit They Are Wrong? Failure Disclosure Is Unstable Under Reinforcement Learning
Steven Y. Feng, Noah D. Goodman, Michael C. Frank et al. (+3 more)
Outcome-based reinforcement learning can produce models with similar task performance but very different ways of communicating about their mistakes. We study failure disclosure: whether a model admits that an attempted solution failed rather than staying silent or presenting it as successful. Across repeated outcome-only GRPO training runs, failure disclosure varies far more than task accuracy. The pattern extends to a second
reasoning task and stabilized PPO, persists at 7B, and also appears in an instruction-conditioned 32B setting. We also find that small floating-point and sampling differences during training can redirect reporting behavior even when the task objective and earlier training history are held fixed. Additional tests show that failure disclosure is not a single decision: Checking the answer, entering a report, and completing the admission can separate, and the weak point depends on the task and response format. Further, experiments with neutral controls show more broadly that behaviors left weakly constrained by training are especially likely to vary across runs, of which failure disclosure is an example. We can reduce variability in failure disclosure by discouraging the model from drifting from its starting policy on failed, well-formed responses. This makes reporting substantially more consistent, though its effect on task performance depends on the setting. Stable task accuracy therefore does not guarantee stable safety-relevant behavior: Researchers should measure these behaviors directly across runs and design training methods that keep them reliable.
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All authors Steven Y. Feng, Noah D. Goodman, Michael C. Frank, Evan Hubinger, Paul C. Bogdan, Andrew Lampinen
Artificial Intelligence · Cross-listed from cs.CV
Scope-WM: Scoped Computation for Efficient Visual World Models
Chunzheng Li, Zesheng Jia, Hongda Zhang et al. (+4 more)
Visual world models enable robotic planning by predicting future observations, but dense latent-state propagation and sample-intensive trajectory optimization incur high inference latency and peak
memory usage, limiting real-time deployment on resource-constrained platforms. Existing sparse world-model acceleration methods either rely on unguided
token sparsification, which may discard planning-relevant information and restrict achievable sparsity, or introduce heavy auxiliary modules and cumbersome multi-stage training pipelines. In this work, we present Scope-WM, an efficient visual world model that scopes computation to prediction-relevant latent regions and promising action sequences. Scope-WM distills prediction relevance into a lightweight action-conditioned selector and applies full dynamics prediction only to a compact subset of selected
tokens. It updates the remaining
tokens using a compact summary of foreground states and their changes, allowing the background to perceive foreground dynamics without costly
token-to-
token interactions. During planning, Scope-WM preserves and reuses high-quality action sequences discovered during the initial MPC search, focusing subsequent search under reduced rollout budgets. The resulting pipeline requires only a one-off selector
distillation followed by a single joint training stage for the sparse world model. On the challenging Push-T task, Scope-WM reduces peak GPU
memory usage and planning time to $18.1\%$ and $14.3\%$ of those of dense DINO-WM, respectively, corresponding to a $6.97\times$ planning speedup, while maintaining competitive task performance. Further evaluations across five diverse visual planning tasks demonstrate the general applicability of Scope-WM. Code is available at https://github.com/ChunZheng2022/Scope-WM.
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All authors Chunzheng Li, Zesheng Jia, Hongda Zhang, Jiaying Tang, Yuntian Wang, Siao Liu, Jin Wang
Artificial Intelligence · Cross-listed from cs.CL
Beyond Calibration: Do a Typed-Decision Model's Probabilities Obey the Probability Axioms?
Keyi Li, Yihao He and Quanyi Li
Typed-decision models such as TypeSafe's Jev answer a declared yes/no or multiple-choice question about a state with a probability instead of text, and their evaluations report accuracy and calibration. Neither requires that the probabilities a model gives to logically related questions fit together item by item, which is what a system that acts on those probabilities needs. We test this property, coherence, with a battery of logically linked questions that needs no labels. For 160 items from ChaosNLI and PubMedQA, each with three mutually exclusive labels, we ask whether the label is X, whether it is not X, whether it is one of the other two labels, and which label applies. On 480 negation pairs, Jev's probabilities for "the label is X" and "the label is not X" miss summing to one by 0.064 on average (95% CI 0.055 to 0.072). Qwen3.8-27B, run from its official BF16 weights, misses by 0.293 with first-
token probabilities and by 0.122 with verbalized probabilities. The gap to the first-
token readout persists on pairs where both systems give similar probabilities, without double-negation labels, and after averaging Jev's repeated calls. Jev is not coherent either: its violations are about five times its repeat noise, and it over-endorses statements about single labels, so that its three single-label probabilities sum to 1.14 on average. The two systems also fail differently. Qwen3.8-27B's first-
token readout under-endorses the complement of a label whether or not the question contains "not", rejecting both a statement and its negation in 196 of 480 pairs, and it does not become more coherent where it is more confident, whereas Jev's violations concentrate where its answer is uncertain. Because the checks need no labels, they expose biases that appear only when question forms are compared, and inconsistencies within items.
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Artificial Intelligence
WorldAgent: Verification-Guided Agentic Physical World Construction
Caoliwen Wang, Mengdi Wang, Yige Chen et al. (+13 more)
Constructing complex physical worlds from
language requires coordinating extensive 3D environments, detailed structures and objects at different spatial scales, and interacting physical processes under both stated goals and implicit physical constraints. We present WorldAgent, an agentic framework for verification-guided physical world construction from a single natural-
language prompt, without iterative user debugging. A world construction layer expands the
prompt into a structured world specification and uses physical knowledge to build scenes and run numerical simulations. After every step, a verification layer inspects scene geometry and simulation states alongside rendered views. Failed checks guide automatic revisions to the specification and re-execution of the affected steps. Accepted worlds pass the required checks and remain editable for further inspection and resimulation. We introduce AgenticSimBench, on which WorldAgent achieves the best scores among the evaluated
agent-based methods on five of seven metrics. In a 26-participant user study, it receives the highest mean ratings across all four criteria.
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All authors Caoliwen Wang, Mengdi Wang, Yige Chen, Zejia Wu, Bowen Huang, Siyuan Chen, Guanxiong Chen, Lifu Wei, Heng Zhang, Qinghai Zhang, Yin Yang, Guandao Yang, Shiying Xiong, Peng Wang, Chenfanfu Jiang, Peter Yichen Chen
Artificial Intelligence · Cross-listed from cs.LG
SMORE: Stability-Promoting Mesh-Agnostic Model Reduction for Time-Dependent PDEs
Yangyuan Li, Weichao Li and Shaowu Pan
High-fidelity simulations of time-dependent partial differential equations (PDEs) are computationally expensive, motivating data-driven reduced-order surrogates for many-query tasks such as uncertainty quantification, design optimization, data assimilation, and optimal control. However, existing surrogate models often exhibit poor temporal stability, which can lead to unstable rollouts and exploding gradients during backpropagation, especially in multistep long-horizon
forecasting. To address this, we propose SMORE, a mesh-agnostic framework for model order reduction of time-dependent PDEs. Its latent dynamics are trained with Lyapunov-guided stability regularization, which promotes stable long-horizon rollouts. We provide theoretical guarantees under the stated structural assumptions. Beyond
forecasting PDE
evolution, the learned latent dynamics, which are interpretable and linear or linear-quadratic, could bring benefits for downstream tasks such as data assimilation and optimal control. Moreover, our framework is capable of predicting continuous PDE solution fields from sparse measurements of the initial condition. We evaluate SMORE on a range of problems, including wave propagation, the Navier-Stokes equations, and the shallow water equations. Our results show that it improves long-horizon rollout generalization and empirical robustness, and achieves competitive accuracy at comparable parameter budgets relative to competitive baselines including DINo, FNO, CNO, and Transolver.
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Artificial Intelligence · Cross-listed from cs.RO
TAO-DA: Towards Autonomous Operation–A Dual-Arm Vision-Language-Action Model for Coordinated Manipulation
Yongsheng Zhao, Han Gao, Baoping Cheng et al. (+7 more)
Vision-
Language-Action (VLA) models provide a unified framework for grounding high-level semantic information into low-level
robot actions, enabling scalable robotic manipulation across diverse tasks. However, existing VLA models lack explicit mechanisms to disentangle the states and intents of the two arms, leading to unintended cross-arm interference that degrades task execution success. To address this issue, we propose a symmetric Dual-Arm Expert (DAE) architecture built upon a shared
Vision-
Language Model (VLM) backbone with decoupled, arm-specific expert towers. Expert selection is carried out through a two-stage dual-arm intent routing scheme, in which experts are routed either by explicit
language instructions in the first stage or by implicit visual semantics in the second stage. Moreover, we introduce a lightweight task progress prediction module that leverages cross-
attention between the pre-chunk temporal features and semantic representations of proprioceptive and visual observations to accurately estimate frame-wise task completion progress. This module facilitates task progress synchronization to support coordinated scheduling for collaborative multi-
robot tasks. Experimental results demonstrate the effectiveness of our model in dual-arm intent routing and the disentanglement of cross-arm interference, and further provide preliminary evidence of emergent skill generalization from single- to dual-arm tasks (as well as the reverse), together with cross-arm motion-domain skill transfer.
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All authors Yongsheng Zhao, Han Gao, Baoping Cheng, Jingyao Tang, Dian Zhou, Deng Liang, Ji Ge, Xuanzhang Wen, Lei Zhao, Ye Wang
Artificial Intelligence
Are Benchmarks Reliable? Toward Structural Diagnosis via Item-Level Capability Boundaries
Haiquan Hu, Yuzhu Liang, Weicheng Tang et al. (+4 more)
Evaluating large
language models (LLMs) relies heavily on
benchmark scores, yet aggregate metrics can obscure whether individual items serve their intended measurement purpose. BSDProbe provides an item-level framework for
benchmark structural diagnosis that jointly characterizes correctness transitions and their supporting response evidence. Repeated responses along an empirically ordered model axis yield complementary correctness and normalized
entropy trajectories, from which four-dimensional profiles of boundary position, boundary width, boundary-signal validity, and order consistency are constructed for
benchmark-level aggregation. Experiments on six
benchmarks identify varying proportions of potentially anomalous items. With Qwen3 as both the selection and reference axis, BSDProbe achieves a 17.9% mean relative improvement in normalized discrimination ratio over the best baseline per
benchmark and matches or exceeds all baselines in ranking agreement with the full
benchmark.
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All authors Haiquan Hu, Yuzhu Liang, Weicheng Tang, Yanzeng Li, Yao Shi, Li Xu, Tian Wang
Artificial Intelligence
Unlocking Latent Personalization in LLMs
Wei Chen, Guanghui Zhu, Zhongliang Cai et al. (+1 more)
Large
language models (LLMs) are increasingly expected to adapt to individual users, yet effective personalization remains challenging when only limited user-specific samples are available. In this work, we take an alternative perspective: pretrained LLMs may already possess latent capacity for personalization, and a few user samples may therefore suffice to guide the model toward user-aligned behavior with minimal user-specific adaptation. From this perspective, we propose LatentPersonal, a framework that formulates personalization as
navigation in a shared latent adaptation space. LatentPersonal infers a compact latent representation from a few user samples to guide user-specific model adaptation, regularized with a variational information bottleneck to encourage compact preference representations. We instantiate LatentPersonal with LoRA, leveraging its low-rank parameterization as a natural low-dimensional adaptation space for personalization. By simply inserting a user-specific guidance vector between the shared low-rank factors, the model can navigate toward personalized adaptations through lightweight inference of this compact representation, without updating the shared LoRA parameters. Experiments across multiple personalization
datasets demonstrate that LatentPersonal substantially reduces user-specific adaptation overhead while achieving effective personalization from only a few user-specific interactions, with particularly strong performance in the one-shot regime.
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All authors Wei Chen, Guanghui Zhu, Zhongliang Cai, Yihua Huang
Artificial Intelligence
SeOPD: Self-Evolving LLMs via Online Policy Distillation from Self-Generated Chain-of-Thought
Xiaoshu Chen, Sihang Zhou, Ke Liang et al. (+1 more)
Recent advances in online policy self-
distillation (OPSD) have demonstrated that large
language models (LLMs) can improve their capabilities by leveraging external privileged information (PI), such as manual annotations or feedback from external environments. However, obtaining accurate annotations and constructing sophisticated environments often require substantial human effort and computation, limiting the scalability of OPSD. While a few recent studies have explored self-improvement without external PI, the resulting gains remain limited. In this work, we explore whether LLMs can achieve comparable self-improvement without external PI. Our key observation is that a single LLM can support multiple
reasoning modes, such as deep-thinking and non-thinking modes, with deep thinking generating additional information during
reasoning. Based on this observation, we propose Self-Evolving Online Policy
Distillation (SeOPD), which enables LLMs to distill and internalize information generated by their own chain of thought (CoT). Specifically, it (1) generates CoT with the deep-thinking mode, (2) produces responses with the non-thinking mode, and (3) uses the generated CoT as PI to provide
token-level supervision for the non-thinking response, allowing new information inferred during
reasoning to guide the non-thinking mode and be internalized into the shared model parameters, thereby improving both non-thinking and deep-thinking capabilities. Extensive experiments across LLMs and tasks demonstrate the effectiveness of SeOPD.
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All authors Xiaoshu Chen, Sihang Zhou, Ke Liang, Xinwang Liu