Research

• Adversarial training47−2.1%
• Federated learning42−2.3%
• Forecasting time72−8.9%
• Frontier LLMs419−18.6%
• Inference & serving240−19.2%
• Anomaly detectors30−21.1%
• Agents466−21.2%
• 3D scene142−21.5%

• Compositionality in RAG pipelines1 paper raises it · 1 addresses it

  Combining individually strong RAG components often fails to improve overall performance, and gains may not transfer across retrieval quality and answer faithfulness.

  • Retrieval Augmented Generation Compositionalityraises · addresses
• Request execution in streaming video generation1 paper raises it · 0 address it

  Models struggle to reliably follow incremental multimodal requests while maintaining state consistency and visual fidelity.

  • StreamEval: Evaluating Interactive Streaming Video Generation Modelsraises
• Brain-body mismatch in recursive self-improving agents1 paper raises it · 1 addresses it

  Evolving execution harnesses may demand behaviors the model has not learned or restrict existing capabilities.

  • CoEvo-RSI: When Harnesses Evolve, Which Models Should Follow?raises · addresses
• Length generalization in state tracking1 paper raises it · 1 addresses it

  Models trained on short sequences often fail to generalize to much longer sequences without explicit progress alignment.

  • PAR: Progress-Aligned Recurrence for Length Generalization in State Trackingraises · addresses
• Evidence-grounded evaluation for audio-visual reasoning1 paper raises it · 1 addresses it

  Existing benchmarks often allow models to answer correctly without analyzing necessary audio-visual evidence, weakening reliability.

  • EvaOmni: An Evidence-Grounded Benchmark for Audio-Visual Reasoningraises · addresses

Named by an AI from these papers' abstracts.

• arXiv cs.CVOct 14

  Discriminative Score Function: Turning Pretrained Models into Functional Generative Priors

  Junhoo Lee, Hyeonjin Kim, Sangbum Han, Nojun Kwak

  Discriminative models are trained with task-specific objectives such as classification, detection, or representation learning, yet their practical utility depends on generalizing beyond the training task to unseen samples from the same domain. This suggests that a discriminative model may retain an implicit trace of the data manifold beyond its task labels. We ask whether this trace can be exposed as a generative signal without task-specific guidance. We introduce the Discriminative Score Function (DSF), which extracts an image-space generative field from the loss-gradient geometry of a pretrained discriminative model. DSF provides a training-free update rule that moves images toward model-consistent visual regions without retraining or task-specific targets. We apply DSF to diverse pretrained discriminative models, including ResNet classifiers, DETR detectors, and DINOv2 ViT encoders. DSF generates unconditional images from noise and supports conditional guidance, editing, inpainting, and explanation through the same field. DSF-generated samples also provide effective zero-shot quantization calibration when real images or labels are unavailable, preserving feature geometry…

  PDF

  Summarize this
• arXiv cs.CVOct 14

  HyperLoop: Hyperspectral-Assisted Loop Closure Detection in 3D-SLAM for Planetary Exploration

  Annie Bhalla, Dennis Rotondi, Riccardo Giubilato, Kai O. Arras, Wolfgang Stuerzl

  Autonomous navigation of planetary rovers is challenged by visually repetitive, featureless terrain. SLAM systems based on 3D Gaussian Splatting (3DGS) offer dense mapping but rely on RGB appearance descriptors that degrade under perceptual aliasing - a common failure mode in extraterrestrial environments where physically distinct locations appear visually identical. This paper presents HyperLoop, the first framework integrating hyperspectral imaging (HSI) into the loop closure module of a 3DGS-based SLAM system. Hyperspectral descriptors encode material-level spectral signatures complementing RGB features, providing robustness to illumination changes and visual repetitiveness. Two HSI descriptor families are introduced: Bag-of-Spectral-Words (BoSW), encoding spectral material distributions via TF-IDF weighted histograms, and PCA-K VLAD (PCAK-VLAD), capturing first-order residual statistics of PCA-compressed spectral signatures. Both are evaluated with SIFT-VLAD and NetVLAD RGB descriptors under Strict-AND and Weighted fusion strategies on a custom dataset from the Moon-Mars Outdoor Test Site (MMOTS) at DLR, comprising synchronized RGB-D and hyperspectral sequences across four…

  PDF

  Summarize this
• arXiv cs.CLOct 14

  Does the Linguistic Register of Thinking Matter? Wenyan as a Compact Reasoning Register for Chinese LLMs

  Minghan Li, Guodong Zhou

  Visible reasoning traces of Chinese reasoning-oriented LLMs are typically written in modern vernacular Chinese, which may be verbose under limited decoding budgets. We ask whether the linguistic register of reasoning can be used to improve efficiency. We propose Wenyan Thinking, which emits intermediate reasoning in Classical Chinese (wenyanwen) while retaining a short direct-answer output. Starting from modern-Chinese reasoning traces, we rewrite only the reasoning field into wenyan and fine-tune Qwen3-4B-Thinking-2507 on the rewritten supervision. To distinguish register shift from generic compression, we construct a compact modern-Chinese control using the same source data, teacher family, base model, and SFT recipe. Across budget-sensitive evaluations on MMLU-ProX-Lite-ZH, CEVAL, and AGIEval-CN, Wenyan Thinking substantially reduces reasoning length and missing-answer rates under constrained decoding. Compact rewriting explains much of the efficiency gain, while wenyan consistently outperforms the compact modern-Chinese control on the primary benchmark and remains competitive across the additional benchmarks. These results identify linguistic register as a practical design…

  PDF

  Summarize this
• arXiv cs.LGOct 14

  ProtModernBERT: Compute-Efficient Protein Language Modeling

  Alberto Martínez, Natalia Flechas Manrique, Llorenç Espinosa-Portalés, Luc Andrea

  Large protein language models provide a powerful framework for learning sequence-function relationships and guiding experimental design. However, architectural improvements are underexplored, especially in low-compute regimes of relevance for resource-constrained research laboratories. We show that the training cost for encoder-only transformer architectures can be reduced by leveraging modern architectural refinements, offering an encouraging performance / compute-efficiency trade-off. We introduce ProtModernBERT, a modernized BERT-style model pre-trained on amino acid sequences, and compare it with ProtBert, Amplify, and ESM2 across a set of supervised structural tasks -encompassing secondary structure prediction, disorder prediction, and subcellular localization- and ProteinGym's zero-shot functional benchmark. We find that ProtModernBERT matches or closely tracks the performance of Amplify-120M and ProtBert with up to 80-90\% reduction in pre-training FLOPs, and narrows the gap to ESM2 when controlling for pre-training. Our analysis using a cluster-based validation protocol for hyperparameter selection and randomly initialized models disentangles the contributions of…

  PDF

  Summarize this
• arXiv cs.CLOct 14

  MakSafe: A Spatiotemporal Semantic Framework for Formal Specification and Compliance Checking of Road Vehicle Safety Rules

  Maksym Labzhaniia, Julien Deantoni, Marie-Agn{è}s Peraldi-Frati, Fr{é}d{é}ric Mallet

  Scenario-based validation is essential for automated road vehicles but cannot cover the full variability of openworld traffic. We present MakSafe, a formal spatiotemporal framework for specifying and checking road vehicle safety rules through semantic zones, action-based forecasting, and trace-tree reasoning, avoiding uniform time discretization. We formalize the road network, vehicle states, and occupancy zones, then build executable rules for front-vehicle identification and timeto-collision (TTC) verification. The evaluation combines a tracedependent case study with a scalability benchmark over traffic density (N ), horizon (H), and branch factor (B). MakSafe yields branch-specific verdicts, deriving safe and unsafe outcomes from the same initial scene, and re-evaluates a scenario after a control update. A single rule check stays well below a millisecond across the reported sweep, and longer horizons slow the TTC check.

  PDF

  Summarize this
• arXiv cs.CVOct 14

  Object Pose from 2-D to 3-D Point and Line Correspondences

  Thai Quynh Phong, Radu Horaud, Adnan Yassine, Pham Dinh Tao

  In this paper we present a method for optimally estimating the rotation and translation between a camera and a 3-D object from point and/or line correspondences. First we devise an error function and second we show how to minimize this error function. The quadratic nature of this function is made possible by representing rotation and translation with a dual number quaternion. We provide a detailed account of the computational aspects of a trust-region optimization method. This method compares favourably with Newton's method which has extensively been used to solve the problem at hand, with Faugeras-Toscani linear method [6] for calibrating a camera, and with the Levenberg-Marquardt non-linear optimization method. Finally we present some experimental results which demonstrate the robustness of our method with respect to image noise and matching errors.

  PDF

  Summarize this
• arXiv cs.MAOct 14

  Exact Cut Complexity of Equal-Length Proportional Cake Cutting

  Yasushi Kawase, Bodhayan Roy, Mohammad Azharuddin Sanpui

  We study proportional cake cutting under an equal-length constraint, where each of $n$ agents must receive a bundle of length exactly $1/n$ that is worth at least $1/n$ to that agent. Our main result determines the exact worst-case number of cuts needed to guarantee such an allocation, namely $2n-2$ for every $n$. This improves on the previously known quadratic upper bound. The proof combines the constrained necklace-splitting theorem with a recursive assignment argument based on Hall's theorem. The same bound extends to the stronger requirement of envy-freeness when $n$ is a prime power, and an $O(n^{1.525})$ upper bound holds for arbitrary $n$. We then ask whether such allocations can be computed. We first show that no deterministic protocol in the Robertson--Webb model terminates after finitely many queries on every instance and always returns an exact equal-length proportional allocation, even for two agents with identical valuations. For every $\varepsilon>0$, however, $O(n^2/\varepsilon)$ queries suffice to produce an allocation in which every agent receives a bundle of length exactly $1/n$ and value at least $1/n-\varepsilon$, again using at most $2n-2$ cuts. Finally, we…

  PDF

  Summarize this
• arXiv cs.LGOct 14

  What Happens to the Goal? Distinct Failures of Policy Alignment in Goal Misgeneralization

  Julia Barth, Armin Sommer, Kunvar Thaman, Jannik Schilling

  Goal misgeneralization is a common failure mode in which an agent behaves competently out of distribution but does not pursue the intended goal. However, we still have a weak understanding of what happens to the intended goal in misgeneralizing agents. Across three Procgen environments and five robot tasks, we find three distinct forms of policy-goal misalignment: (1) functional target substitution, where behavior follows an alternative goal, (2) a policy shortcut, where behavior follows a learned action strategy, and (3) cue competition between intended and proxy goal cues. We distinguish these regimes by tracing where goal information remains available internally, how strongly it influences the policy, and what role it plays in action selection, and find that the goal is lost at different depths. Where the goal reaches the decision, as in cue competition, steering redirects the policy to the goal's location, raising intended-goal reach from 16.0% to 95.3%. Where it reaches the policy but not the decision, only new downstream wiring recovers goal reach, from 31.6% to 84.3% in the environment with the action shortcut. Where it is inert, neither works. Our results show that goal…

  PDF

  Summarize this
• arXiv cs.CVOct 14▲ 17

  A Diagnostic Study of World-Space Human Recovery Under High Camera Dynamics

  Hao-Jen Chien, Wei-Lun Chao, Yu-Lun Liu

  Monocular world-space human recovery methods excel on current benchmarks, but these rarely include fast camera motion. We introduce the K-Pop stress test, broadcast dance videos that combine fast, large camera motion with dense multi-person choreography. Leading methods produce plausible per-frame meshes on these videos, yet their world-space trajectories drift far from the performers' stage positions. Through a systematic diagnosis, we show that the camera and human estimates each remain self-consistent, and that the drift appears when they are combined at mismatched scales. Each is recovered at its own scale, so a mismatch between the two can turn camera motion into human drift that grows with camera displacement and becomes severe in broadcast dance videos. We derive a closed-form correction that estimates this mismatch and applies one scale to all performers, removing the drift while keeping their relative positions in one shared world frame. It requires no retraining and works with existing backends. Combined with physical constraints, the correction yields stable world-space reconstruction for choreography modeling, scene editing, data generation, and virtual reshooting.…

  Code
• arXiv cs.CVOct 14

  Mitigating Video Hallucinations via Attention-Guided Representation Perturbation

  Zhixin Cai, Ruoqi Yuan, Xinchen Yang, Zhuofan Chen, Jun Bai +2

  Video large language models (Video LLMs) often generate responses unsupported by the input video. Contrastive decoding (CD) can mitigate these hallucinations, but its effectiveness depends on how the negative branch weakens visual evidence. Existing methods rely on predefined perturbations or masking guided by question reconstruction, offering limited control over the model's visual reliance during answer generation. We introduce Attention-guided Representation Perturbation for Contrastive Decoding (ARP-CD), a training-free framework for constructing contrastive negatives directly in representation space. ARP-CD uses the attention mass assigned to visual tokens at the answer-prediction position to guide bounded gradient updates of the language model's input visual hidden states. These updates reduce visual attention and produce a negative input, whose predictions are contrasted with those of the original branch throughout generation. Experiments across four Video LLMs and four benchmarks show that ARP-CD outperforms representative training-free baselines in most model--benchmark settings, reducing hallucinations while improving general video understanding. Further analyses show…

  PDF

  Summarize this
• arXiv cs.CVOct 14▲ 8

  Generative World Simulation with DAO and JING

  XGEN Team, Guohao Cai, Haoxuan Che, Frank Fan, Chenran Huang +14

  We present a Generative World Simulation framework that couples persistent shared world state with continuous first-person audiovisual generation. The framework integrates DAO, a computable world engine, and JING, an Egocentric Interactive Experience Model, to support sustained interaction for humans and agents. DAO maintains the states of characters, objects, and spaces and their relationships, advancing the world according to interaction rules and world time while preserving the consequences of past actions. From this evolving state, DAO derives observer-specific conditions for JING, which combines these conditions with character, object, and scene references, observation history, and current actions to generate each observer's audiovisual experience. Users can explore environments, manipulate objects, and communicate with characters through viewpoint control and text instructions, with their actions shaping both the shared state and subsequent experiences. By connecting world-state evolution with experience generation in a continuous interaction loop, the framework offers an approach to building interactive worlds that retain their history, evolve through action, and persist…

  Code
• arXiv cs.LGOct 14▲ 3

  TimeRSI: Towards Self-Improved Agentic Deployment for Time-Series Foundation Models

  Haoxin Liu, Deqing Fu, Lucheng Fu, Yiqiao Jin, Rushi Qiang +2

  Time-series foundation models provide strong zero-shot forecast, but deployment for real-world applications still requires task-specific designs for model inputs and outputs. Direct inference can leave domain knowledge and recurring forecast errors unaddressed, while tailored pipelines require substantial human expertise and effort. We introduce TimeRSI, a self-improving agentic deployment system around frozen forecasting models. A coding agent translates task descriptions into executable pipeline changes. It tests each revision on historical forecasting windows and uses observed errors to guide subsequent revisions. During deployment, an adaptive selector chooses among the resulting pipelines using completed forecasts under similar conditions. Across 198 tasks on TIME and fev-bench, TimeRSI outperforms all submissions on six point and probabilistic forecasting metrics. Controlled comparisons support the value of feedback-guided revision over non-iterative pipeline search. Further experiments verify the generalization with different coding agents and forecasting models. Code is available at https://github.com/haoxin1998/TimeRSI.

  Code
• arXiv cs.CLOct 14

  Recognizing Dialogue Ambiguity via Candidate Set Visual Grounding

  Shun Inadumi, Lee Sangmyeong, Koichiro Yoshino

  Interpreting an ambiguous utterance in real-world interactions often requires context beyond a single utterance. When the utterance refers to objects in a visual scene, it is necessary to maintain and update the set of plausible referents as context accumulates, without assuming that the available information identifies a unique object. However, prior work on interactive visual grounding, which localizes objects referred to by linguistic expressions, has focused on identifying a unique object once sufficient dialogue context becomes available. To address this gap, we propose Candidate Set Visual Grounding (CSVG), a task for estimating the set of objects consistent with the given dialogue context. We further introduce CandVG, a benchmark that evaluates how the candidate set changes across dialogue turns. Our evaluation of vision-language models shows that stronger visual grounding capability does not necessarily yield better CSVG performance. In particular, models have difficulty predicting the complete candidate set, owing to challenges in candidate generation and premature commitment to a small set of candidates in early turns. Nevertheless, we find that this limitation can be…

  PDF

  Summarize this
• arXiv cs.SDOct 14

  ONCship: A Large-Scale Underwater Acoustic Corpus for Ship Recognition

  Zhe Cao, Jichao Zhang, Xiao-Lei Zhang

  Recent advances in deep learning have significantly improved underwater acoustic vessel recognition. However, existing publicly available datasets are typically limited in scale, hindering large-scale representation learning and downstream task evaluation. To address this limitation, we introduce ONCship, a large-scale underwater acoustic dataset comprising 4521 hours of curated recordings from Ocean Networks Canada (ONC) hydrophones and Automatic Identification System (AIS) data. We evaluate the proposed dataset across three tasks: self-supervised representation learning, vessel-type classification, and MMSI-Level Vessel Classification. Experiments on DeepShip and ShipsEar demonstrate the effectiveness of ONCship for acoustic representation learning, with joint pre-training on ONCship and AudioSet achieving a classification accuracy of 62.45% on DeepShip through linear probing. Furthermore, incorporating ONCship as auxiliary training data improves downstream vessel-type classification performance. Beyond conventional vessel-type classification, we further investigate MMSI-Level Vessel Classification, treating each vessel as a distinct identity. Experimental results show that…

  PDF

  Summarize this
• arXiv cs.AIOct 14

  Articulation Upper Bounds for Language Models

  Alexander Spangher, Daniel E. Ho, Diyi Yang, Noah Goodman, Sanmi Koyejo

  Language captures only a subset of the perceptions, intuitions and intents that form meaning between speakers. What, then, are the limits of language? We take steps towards measuring this in LLMs as follows: for a set of concepts, we measure how well each concept can be transmitted between two LLMs as their capabilities scale. This requires novel measurement tools: (1) optimal messages: we introduce a prompt optimization algorithm that beats existing algorithms by up to 14% on benchmarks and accurately estimates prompt upper bounds. (2) A transmission measure: the sender is asked to label text; the receiver then tries to recover the sender's prompt, and recovery is measured; we find this correlates strongly with human label error. With these measures, we use observational scaling laws to study how transmissibility of a concept, conveyed with optimal messages, increases as transmitter and receiver capacity grows: does it asymptote below or above a threshold? Measuring 1.2k concepts in eight domains, we find concepts that (a) can be fully articulated; these concepts usually depend on local patterns, e.g. "joke ends with surprise"; (b) might be articulated by future, stronger…

  PDF

  Summarize this
• arXiv cs.AIOct 14

  Learning Individual Scientific Taste with Privileged Hindsight Distillation

  Hanwei Fan, Yanqiao Wang, Yiqi Zhu, Xuanyu Lei, Peng Li +1

  Scientific taste shapes which research directions researchers pursue, and it varies across individuals. Existing work evaluates research directions along multiple evaluation criteria or models scientific taste using community feedback signals such as citations and reviews, but neither directly captures how individuals translate assessments under the same criteria into different preferences. We model scientific taste as a researcher-specific mapping from criterion-level assessments in a shared, evidence-grounded evaluation space with proposal-visible evidence, to relative preferences. A population-level mapping learned from retrospective supervision provides a cold start when individual feedback is scarce. We introduce Privileged Hindsight Distillation (PHD), which uses later-emerging scientific evidence during training to improve proposal-time criterion assessment and the population mapping, while exposing only proposal-visible information at inference. We evaluate our approach primarily in quantum research, where structured public reviews are largely unavailable, and use pairwise proposal comparison as a direct interface for observing individual taste. With only 15 comparisons…

  PDF

  Summarize this
• arXiv cs.CVOct 14

  No Way To Steal My Face: Proactive Defense Against Identity-Preserving Personalized Generation

  Lizhi Xiong, Jun Li, Ziqiang Li, Weiwei Jiang, Zhangjie Fu

  Recent advances in diffusion models have enabled high-fidelity, identity-preserving image generation for personalized applications such as digital avatars and virtual try-on systems. However, their reliance on sensitive facial reference images raises growing privacy concerns. Existing defense mechanisms are primarily designed for training-based personalization and struggle to generalize to emerging training-free approaches, due to fundamental differences in their identity integration paradigms. To bridge this gap, we propose IDGuardian-the first generalizable and model-agnostic identity protection framework capable of defending against both training-based and training-free personalization methods. IDGuardian abstracts the personalization process into two critical stages: identity extraction and identity injection. It then introduces crafted adversarial perturbations to simultaneously disrupt both stages. Specifically, it degrades the identity features extracted by external encoders and establishes an adversarial conceptual bridge that misdirects the generative trajectory away from the target identity. Extensive experiments show that IDGuardian effectively protects identity across…

  PDF

  Summarize this
• arXiv cs.ROOct 14

  Uncertainty-Driven Self-Play for Robust Multi-Agent Driving Policies

  Sajad Marvi, Julian Schmidt, Alessandro Canevaro, Julian Jordan, Abhinav Valada

  Learning robust driving policies that generalize beyond their training domain remains a central challenge in motion planning. We propose an uncertainty-driven self-play scheme, in which half of the agents are rewarded for increasing their neighbors' epistemic uncertainty, while the other half learn to drive safely as the exploring agents continuously seek out their weaknesses. Epistemic uncertainty indicates where the policy is least confident and serves as an automatic proxy for scenario difficulty. Exploring agents use this signal to seek out hard cases, generating a challenging curriculum without manual scenario design. The resulting policy outperforms standard self-play, achieving best-in-class zero-shot transfer to nuplan Val14 (99.12% vs. 97.97%) and Argoverse 2 (82.39% vs. 78.03%) without fine-tuning, while also improving performance over standard self-play on Waymo. Our diversity-driven approach achieves 85% higher LSTM hidden-state coverage than the baseline, indicating a much richer set of behaviors. Further analysis shows that epistemic uncertainty serves as a calibrated difficulty detector, with highest-uncertain agents exhibiting 14.8 times the combined failure rate…

  PDF

  Summarize this
• arXiv cs.LGOct 14

  New Model, Old Habits: Behavior Transfer under Model Swapping in Multi-Session LLM Agents

  Youngbin Choi, Sehyun Hwang, Suhyeon Jun, Seoyeon Lee, Sunghyun Choi +2

  Multi-session agents can switch between LLMs to balance performance and inference cost. Such switches require transferring a history for continuity, yet their effects on the recipient remain underexplored. Through controlled experiments with multi-session tasks, we uncover behavior transfer: recipients tend to adopt reasoning and action patterns from the models that generated their histories. This effect is most evident with raw-history transfer but is substantially reduced by summarization. Based on these findings, we propose Direction- and Authorship-aware Summarization of History (DASH) that preserves stronger-model behavior while minimizing weak-to-strong transfer via summarization. Experiments with repeated swaps show that DASH outperforms raw-history transfer and naive summarization baselines. These results suggest that effective multi-model orchestration requires considering not only which model to use, but also which behavioral patterns to carry forward.

  PDF

  Summarize this
• arXiv cs.CLOct 14

  Beyond State Capacity: Why Retrieval Breaks in Mamba

  Nir Yarden, Amit Ben-Artzy, Roy Schwartz

  Mamba models underperform Transformers on in-context retrieval. This gap is usually attributed to Mamba's fixed-size recurrent state, which loses information as the context grows. We challenge this assumption by focusing on query-first retrieval, a setup in which the query precedes the context and state capacity should not be the binding constraint. We show that even in this setting Mamba lags substantially behind Transformers, despite its selection mechanism, which was designed to distinguish relevant content from distractors. We then demonstrate that the Mamba selection gates have sufficient control over the state updates to influence the model output: intervening on specific gates alone can swing accuracy between extremes. However, as gates are input-dependent, they require the incoming token's representation to encode its relevance to the query. We therefore probe token representations across layers, and find that the relevance signal emerges only gradually with model depth, and that the gates begin to reflect it only several layers deeper. Crucially, we find that gates in retrieval-critical layers are not yet aligned with relevance. The selection mechanism is thus expressive…

  PDF

  Summarize this