Research

• General in-context learning for embodied agents1 paper raises it · 0 address it

  Enables models to adapt to new tasks and environments without retraining, improving robustness in real-world deployment.

  • Has GPT-6-Astra Achieved Embodied Intelligence?raises
• Cost and latency in embodied control loops1 paper raises it · 0 address it

  High computational and time costs limit real-time applicability of agentic systems in dynamic environments.

  • Has GPT-6-Astra Achieved Embodied Intelligence?raises
• Physical-state verification and failure recovery1 paper raises it · 0 address it

  Agents must reliably detect and correct errors in physical interactions to avoid irreversible failures in real-world tasks.

  • Has GPT-6-Astra Achieved Embodied Intelligence?raises
• Long-horizon task preservation in embodied agents1 paper raises it · 0 address it

  Maintaining progress over extended sequences is critical for complex, multi-step tasks in robotics and navigation.

  • Has GPT-6-Astra Achieved Embodied Intelligence?raises
• Open-source stacks for generalist computer-use agents1 paper raises it · 1 addresses it

  Closed systems hinder reproducibility and systematic study of agent design for real-world computer tasks.

  • OpenGCUA: Toward a Fully Open Stack for Generalist Computer Useraises · addresses

Named by an AI from these papers' abstracts.

• Training & fine-tuning46+155.6%
• Computer vision36+140.0%
• AI safety33+135.7%
• Robotics22+120.0%
• Small LLMs8+100.0%
• Evals67+63.4%
• Image & video generation23+43.8%
• Code models14+40.0%

• arXiv cs.LGOct 17

  TideKV: Delayed KV Eviction via Layer-Wise Chunked Prefill for Memory-Efficient Long-Context LLM Inference

  Yu Wang, Zhu Wang, Ning Hu, Yifan Guo, Xiu Ma +2

  Long-context large language model (LLM) inference is important for modern intelligent applications but remains constrained by peak memory during prompt prefill. Existing KV cache compression reduces decoding memory but typically requires full-prompt prefill to preserve global context, retaining a high construction peak. Online chunk-level eviction lowers memory earlier but selects before each layer has observed the complete prompt, potentially degrading cache construction and decoding. We present TideKV, an on-GPU cache-construction architecture using layer-major delayed eviction. TideKV processes all prompt chunks within each Transformer layer, retains full-prompt KV only for the active layer, and evicts at layer end. This schedule preserves full-prompt-aware selection while reducing activation and temporary memory. Experiments across multiple computing platforms show that TideKV preserves the inference quality and final cache size of the evaluated compression methods. At 64K on A100, it reduces the construction-time transient footprint by 83.89%. On RTX 4090, it increases time to first token by only 1.06% at 64K and enables 128K prefill. TideKV also completes 128K on Jetson,…

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• arXiv cs.LGOct 17

  CoTs remain monitorable when being unfaithful requires computation: Replicating and Extending the Necessity Effect Across Eleven Models

  Arav Dhoot, Yixiong Hao, Zephaniah Roe

  While a LLM's chain of thought (CoT) is a promising approach to interpreting model outputs, prior work shows CoT's content does not always accurately reflect the reasoning process. Emmons et al. (2025) finds that models can follow a question hint without revealing so in its chain of thought, but when the hint is sufficiently complex the computation is forced into the CoT. This claim, however, has only been tested on Gemini models, and the datasets and code is closed source. We replicate their proxy task across eleven models from six families and find the necessity effect generalizes. Every model follows simple cues above a no-hint baseline, while concealed complex-cue following falls back near baseline for most models. We also extend the study from Emmons et al. (2025) to measure CoT-necessity and unfaithfulness across five cue families (four of them new) and three difficulty levels. The relationship between when CoT is necessary and unfaithfulness is task-specific and model-specific. For some hint types, models often verbalize their hint-following behavior even when the hint can be decoded in a single forward pass. These results support the case for CoT monitoring when unsafe…

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• arXiv cs.CVOct 17

  CodecMotion-OAD: Efficient Online Action Detection with Compressed-Domain Motion

  Zepeng Sun, Naichuan Zheng, Hailun Xia, Junjie Wu

  Online action detection (OAD) identifies ongoing actions in streaming videos. Many existing OAD systems combine RGB appearance with optical flow, but dense-flow extraction remains a major computational bottleneck. Codec motion vectors (MVs) are available during decoding, avoiding a separate optical-flow computation. On THUMOS'14, optical flow exceeds MVs by 11.2 mAP points with motion-only inputs, whereas the gap narrows to 3.7 points after RGB fusion, with the largest reduction in action-category discrimination. We propose CodecMotion-OAD, which replaces optical flow with decoder-provided MVs while retaining existing temporal detector architectures. Its two-stage training first aligns MV features with a pretrained flow-feature interface without action labels and then trains RGB+MV detectors using ground-truth labels and soft predictions from a frozen RGB+Flow teacher. To the best of our knowledge, this is the first application of codec MVs to frame-level OAD. On THUMOS'14 with MiniROAD, CodecMotion-OAD achieves 68.3 mAP versus 70.9 mAP for matched-distillation RGB+Flow, while reducing decoder-to-prediction processing time by 11.48-fold on CPU and 2.04-fold on GPU.

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• arXiv cs.AIOct 17

  ConCI: A Controlled Counterfactual Benchmark for Contextual Integrity in LLMs

  Juhyeon Park, Sumin Yu, Taesup Moon

  Contextual integrity (CI) holds that the appropriateness of sharing personal information depends on the context of the information flow, not on the information alone. However, existing CI benchmarks often use independent scenarios that vary multiple CI parameters simultaneously and are dominated by inappropriate flows, making it unclear whether correct predictions reflect sensitivity to context or a general refusal tendency. We present ConCI, a controlled-counterfactual benchmark in which an anchor flow is paired with two counterfactuals that each modify a single CI parameter. The resulting triplet is scored jointly, with credit given only when all three flows are judged correctly. Built from six existing benchmarks, ConCI contains 1,592 flows in 556 triplets, is approximately label-balanced, and is validated by seven human annotators per flow. Across seven LLMs, many correct anchor predictions fail on at least one counterfactual, and even the strongest model errs on about a fifth of triplets. Furthermore, models perform near ceiling on inappropriate flows but weaker on appropriate ones, often continuing to judge a flow inappropriate even when a contextual change should make it…

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• arXiv cs.CLOct 17

  Backend-Frontend Converter: Component-Based Conversion of .NET Computational Models to TypeScript

  Petr Ivankov, Max Shestov

  Computational models developed in .NET often need to run locally within web applications. We present Backend--Frontend Converter, an architecture for generating TypeScript implementations of component-based models. The converter maps components, connections, aliases, and formula trees to model-specific TypeScript code. Reusable runtime libraries provide numerical integration, data access, and state handling. We evaluate stored generated implementations through four orbital scenarios and a recorded trading-strategy comparison. Orbital calculations execute in a browser and are paired with C\# results by crossing event and timestamp; trading outputs are aligned by date. Analytic scheduler tests and deliberately perturbed outputs assess the validation procedure. Each orbital scenario produces 91 paired events with identical timestamps. Position and velocity differences remain below $(10^{-6}\)~km$ and $(10^{-9}\)~km/s$. All 20 analytic scheduler checks pass, and the comparator detects each of three prescribed state and time perturbations. The trading comparison contains 501 aligned daily records with matching indicator values, positions, and order events. The evaluated…

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• arXiv cs.LGOct 17

  VBO-MI: A Differentiable Black-Box Optimization Framework via Variational Mutual Information Estimation

  Farhad Mirkarimi

  Many real-world tasks require optimizing expensive black-box functions accessible only through noisy evaluations, a setting commonly addressed with Bayesian optimization. While Gaussian processes(GPs) and Bayesian neural networks have driven significant progress, GP-based methods scale cubically with observations, and BNN-based approaches remain burdened by expensive posterior sampling such as Hamiltonian Monte Carlo, and inner-loop acquisition optimization in high-dimensional setting. In this work, we propose VBO-MI, a fully gradient-based BO framework that requires no assumptions on a Gaussian prior or a variational posterior family over $f$. While our framework uses gradient-based updates to train the internal action and information-estimation networks, it treats the objective function $f$ as a strict black box, requiring only zeroth-order evaluations. To enable end-to-end gradient flow, we employ an actor-critic architecture consisting of an action-net to navigate the input space and a variational critic to estimate a surrogate information signal for exploration. Additionally, we design a lightweight surrogate that approximates the objective from observed data using a replay…

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• arXiv cs.CLOct 17

  Vocal-Tract Acoustics, Aerodynamics, and Overlap-and-Add Synthesis in DYNARTmo

  Bernd J. Kröger

  This paper presents the acoustic and aerodynamic extension of DYNARTmo, a dynamic articulatory model for visualizing and synthesizing speech production. Gestural control trajectories determine vocal-tract geometry, which is converted into an acoustic tube with nasal branching and effective lateral passages. A lumped aerodynamic network relates prescribed glottal motion and pulmonary pressure to airflow and supraglottal pressure storage. Phonatory excitation, oral friction, and glottal aspiration are synthesized as separate contributions. Frame-specific acoustic responses are combined by overlap-and-add, preserving response tails beyond individual control frames. Three German words, ``Tasse'', ``Muse'', and ``Lade'', illustrate the coordination of articulation, pressure, flow, and acoustic sources across vowels, nasals, laterals, fricatives, and plosives. The implementation has been optimized by the author primarily for sound recognizability; independent perceptual evaluation remains outstanding, and fricative-noise naturalness requires further refinement. The model provides an inspectable, articulatorily controlled production plant for future neural simulations of babbling,…

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• arXiv cs.CLOct 17

  Modeling Discourse Profiles in Climate Misinformation Discourse

  Ge Gao, Zhengyang Shan, Bryan A. Plummer, Margrit Betke, Derry Wijaya

  Prior work on climate misinformation detection typically classifies individual comments or claims using text alone. This framing overlooks a key property of online climate discourse: misinformation is often produced, reinforced, and sustained through recurring rhetorical patterns that reappear across comments and threads. Using a large-scale TikTok climate discourse dataset, we construct discourse profiles by clustering climate-related comments into recurring rhetorical patterns. We then incorporate these profiles into fine-grained climate misinformation classifiers using a late-fusion architecture. Under video-disjoint evaluation across three training seeds, discourse-profile-aware models significantly improve macro-F1 over text-only baselines for bag-of-words, TF-IDF, and MPNet representations. RoBERTa-large exhibits a positive mean improvement but substantially greater variability, and the difference is not statistically significant. Beyond classification, our results show discourse profiles shape misinformation reception and propagation. Discourse-profile-conditioned language model agents exhibit misinformation acceptance patterns aligned with profile-level misinformation…

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• arXiv cs.LGOct 17

  Steerable Pluralistic Policy Alignment from Anonymized Crowd Feedback

  Akansha Kalra, Ellen R. Novoseller, Vernon J. Lawhern, Daniel S. Brown

  Pluralistic alignment requires AI systems to learn from diverse populations whose preferences may reflect heterogeneous or even conflicting objectives, and to personalize their behavior to individual users. Existing approaches in pluralistic alignment often assume access to annotator identities and rely on powerful pretrained representations to model individual preference variations. We propose Preference-PCA Pluralistic Learning (P3L), a novel framework for pluralistic alignment that jointly learns trajectory embeddings and the shared basis of preference directions via PCA decomposition from heterogeneous crowd feedback without requiring annotator identities. At test time, our approach enables rapid and accurate personalized reward learning and downstream policy optimization. Experiments across multiple embodied control tasks demonstrate that P3L enables steerable personal alignment that generalizes beyond the offline dataset to out-of-distribution new users and attains competitive RL and test performance, while substantially reducing computational cost and supporting variable adaptation query budgets.

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• arXiv cs.AIOct 17

  MedPAO-Assist: Edge-Deployable, Modality-Aware Agentic AI for Radiology Report Structuring

  Shrish Shrinath Vaidya, Minmini Selvam, Ganapathy Krishnamurthi

  The adoption of Large Language Models for radiology report structuring has been largely dominated by either proprietary cloud-based models or large open models requiring high-end GPUs for deployment, which is a critical bottleneck for clinical adoption in radiology workflows that demand data privacy and offline operation. To address this, we present MedPAO-Assist, an edge-deployable agentic framework that generates structured radiology reports directly from radiologist dictation and voice commands, requiring only $\approx3.3$ GB peak RAM and achieving an average end-to-end latency of $\approx31$ seconds on a standard laptop CPU through an asynchronous, multi-threaded execution pipeline. The system currently supports four major imaging modalities -- Chest X-ray, Chest CT, Abdomen CT, and Brain MRI. To our knowledge, it is among the earliest agentic systems to run its entire pipeline with a Small Language Model (SLM) in the 0.6B-parameter range. This multi-agent architecture, built around an Extract-Ground-Generate paradigm, performs concept extraction, SNOMED-CT grounding, and structured report generation, followed by a speech-driven human-in-the-loop correction phase, together…

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• arXiv cs.CLOct 17

  Write Once, Check Many: Reusable Ledgers for Post-Hoc Policy Compliance Auditing of Agent Trajectories

  Ashwin Nagarajan, Gursimran Panesar, Alicia Yang, Mona T. Diab, Virginia Smith +1

  Auditing LLM agents against policies is critical to ensure safety but requires rereading long trajectories once per policy. Can we instead record each trajectory once and reuse that record for policies not known in advance? We propose write once, check many, a framework in which a writer model converts each agent trajectory into a policy-independent ledger of the agent's actions and outcomes. A reader then checks each new policy against the ledger alone, without access to the original trajectory. On 900 trajectories from SHADE-Arena, $τ$-bench, and $τ^2$-bench, we show that a 12B reader of narrative ledgers can either match or in fact achieve higher AUC than the same 12B model reading full trajectories. On SHADE-Arena policies that combine several facts, our approach outperforms a 31B model reading the trajectory (.777 vs. .713 AUC). When checking eight SHADE-Arena policies separately, ledgers use about $5\times$ fewer tokens, including the cost of writing them. A deeper analysis shows that rule-based extraction performs better on some single-action policies, and ledgers can miss evidence in long tool outputs and message contents. Our results suggest that reusable,…

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• arXiv cs.CVOct 17

  RiSE: Residual Subspace Expert for Generalizable Text-Centric Image Forgery Localization

  Kahim Wong, Kemou Li, Yiming Chen, Haiwei Wu, Jiantao Zhou

  As AI-assisted image editing becomes increasingly prevalent, Text-Centric Image Forgery Localization (TFL) is essential for protecting trust in financial and legal records. However, we observe that existing TFL detectors predominantly rely on Full-Parameter Fine-Tuning (FPFT) and fail to generalize to unseen forgery types because FPFT can induce low-rank feature collapse and overfit training artifacts. Meanwhile, prior detectors capture diverse forgery traces with a single unified representation, making subtle traces entangled and difficult to learn. To address these challenges, we propose RiSE, a Residual Subspace Expert model that preserves pre-trained priors by freezing principal SVD components and adapting only the residual subspaces with a ViT-only segmentor to avoid feature collapse. RiSE introduces forgery-aware residual experts, where each expert is trained independently on a training subset with a particular forgery type, and we show that cross-domain localization performance improves consistently as more experts are added. At inference, we introduce Latent Perturbation Confidence (LPC), which selects the most confident expert by analytically computing the…

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• arXiv cs.SEOct 17

  Effective Construction of Feature Database of Fingerprinting Functions for Code-Clone SCA

  Zhiling Zhu, Tieming Chen, Chengwei Liu, Jiahui Wu, Qijie Song +3

  In contemporary software development, the extensive adoption of third-party libraries has become standard practice to avoid reinventing the wheel. This trend is particularly evident in programming ecosystems that lack mature package managers, such as C/C++. Within this context, Code Clone Detection (CCD) techniques have emerged as essential tools for identifying instances of code reuse, thereby mitigating security and legal concerns. Despite significant advances in CCD algorithms, a notable gap persists between academic tools and real-world applications. This disparity primarily arises because academic tools rely on experimental datasets composed of a limited number of indexed repositories, whereas real-world applications require a much broader scope of code sources. To address this challenge, we propose a novel, systematic approach for selecting appropriate open-source repositories to construct a more effective CCD feature dataset. Our method, OSSScope, employs a greedy algorithm to analyze the diversity of fingerprinting functions across 2.55 million C repositories on GitHub, maximizing unique fingerprinting functions while minimizing the number of repositories required.…

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• arXiv cs.CLOct 17

  Beyond Recall Saturation: Scaling Hard-Negative Reranker Training for Industrial Legal Retrieval

  Yunhan Li, Mingjie Xie, Zeyang Shi, Gengshen Wu, Min Yang

  Industrial retrieval systems often improve quality by enlarging embedding candidate pools or adopting complex training pipelines. We show that, under a sufficiently large but practical candidate pool, embedding recall exhibits diminishing returns, motivating a closer examination of reranker quality. On Chinese legal retrieval benchmarks, embedding top-300 reaches a high-recall operating region, while relevant evidence may still be ranked below the output cutoff. We compare hard-negative strategies for reranker training, including embedding-mined, self-mined, and dynamic-negative variants, under aligned retrieval protocols that evaluate reranking after candidate retrieval. Among the evaluated staged configurations, an embedding-to-self-mined pipeline provides a favorable trade-off; additional dynamic stages do not improve all held-out metrics. The selected pipeline scales from a 0.6B to a 4B reranker. Relative to the native 4B reranker, the trained 4B Stage2 model improves reported metrics on the STARD and CSAID benchmarks and on an internal development set, and is preferred in 72.4\% of non-tied comparisons on 410 de-identified, reconstructed internal operational-query inputs in…

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• arXiv stat.MLOct 17

  Private Evaluation of High-Dimensional Functions

  Mohammad Afzali, Hassan Ashtiani, Adam Smith

  We study differentially private black-box evaluation of arbitrary high-dimensional functions. Given a dataset $S$ and query access to a function $f$---whose sensitivity may be unknown or unbounded---the goal is to privately output an approximation to $f(S)$. The black-box evaluator can only query the value of $f$ over a down neighborhood of $S$ (i.e., those subsets of $S$ that are obtained by removing a bounded number of elements from $S$). We design generic black-box evaluators for both pure and approximate differential privacy through a reduction to high-dimensional private quasi-concave optimization (QCO). Our black-box evaluator enjoys a geometric notion of utility. Consider the vectors obtained by applying $f$ on sets in a down neighborhood of $S$. With high probability, the evaluator's output is guaranteed to be in the convex hull of these vectors. Our work generalizes the result of Linder et al. [LRSS25] for real-valued function evaluation to high dimensions. It also recovers the private center-point guarantees of Beimel et al. [BMNS19], with an explicit additive approximation to maximum Tukey depth. We also apply our evaluators to user-level quasi-concave and convex…

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• arXiv cs.SEOct 17

  Safety Analysis of CPSs Over-the-air Updates: Test Case Prioritization and Selection

  Andrea Bombarda, Luca Carminati, Nunzio Marco Bisceglia, Mehrnoosh Askarpour, Sahar Kokaly +3

  Over-the-air (OTA) updates for Cyber-Physical Systems (CPS) allow engineers to modify their software wirelessly. In the automotive domain, they enable manufacturers to update software without physical access to the vehicle. However, OTA updates can generate a large number of possible software configurations. For example, end users may decide whether or not to install any given OTA updates. The combinatorial explosion of these configurations hampers SW testing, a standard practice for detecting errors in CPS. To overcome this limitation, we propose CRISP (CRIticality-based Selection and Prioritization), a novel framework supporting test case prioritization and selection in the context of OTA updates. Our framework leverages test case robustness, i.e., how far they are from violating the system requirements, to drive test case prioritization and selection. It enables engineers to rank test case executions from most to least critical, and to select only the critical test cases relevant to a given OTA update. We evaluated our approach using a representative SW product line consisting of seven different vehicles, 22 driving scenarios, and two software components. We considered 224…

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• arXiv cs.CLOct 17

  Ben-Misandry: A Bengali Dataset and Late-Fusion Transformer Framework for Detecting Anti-Male Gender Hate

  Md Mehedi Hasan Nipu, Chinmoy Mitra, Tarannum Ahmed Nowshin, Israt Moyeen Noumi

  Gender based hate speech research in Bengali has focused almost exclusively on misogyny, leaving anti male hate, or misandry, entirely unstudied for this language and creating a systematic blind spot in moderation systems trained to catch attacks on women but not on men. We introduce Ben-Misandry, the first Bengali misandry detection dataset: 5,000 comments across six taxonomy classes, including a novel category for mocking male emotional vulnerability with no direct analogue in existing misogyny taxonomies. The corpus is taxonomy-controlled synthetic data generated with GPT-4.1, spot-checked by four human reviewers on a 500-comment sample who confirmed the large majority of assigned labels; a full inter-annotator- agreement study is left to future work. We benchmark it with a classical pipeline (seven classifiers over four text representations) and a transformer pipeline (BanglaBERT, XLM-RoBERTa, and two fusion strategies). The best classical model, a linear SVM over character-level TF-IDF, reaches 91.8% accuracy; a late fusion ensemble averaging BanglaBERT and XLM-RoBERTa softmax outputs reaches 96.6% accuracy and macro ROC-AUC above 99%, nominally ahead of every single model…

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• arXiv cs.AIOct 17

  Pluralistic Alignment in Language Models: Objectives, Human References, and Preference Representations

  Meng-Chen Wu, Umesh Bodhwani

  Pluralistic alignment aims to represent meaningful variation in human preferences rather than collapse heterogeneous judgments into a single target. Yet existing work often mixes three distinct questions: what pluralistic behavior a model should exhibit, whose preferences define the reference, and what evidence is sufficient to demonstrate success. We organize the literature around a two-layer taxonomy. Layer 1 distinguishes representing a range of human-supported viewpoints (Overton Pluralism), adapting faithfully to a specified perspective or preference condition (Steerable Pluralism), and matching the response distribution observed in a target population (Distributional Pluralism). Layer 2 distinguishes aggregate populations, countries and regions, and subgroups as human references. Preference representation, implementation mechanism, modality, and evidence quality are treated as cross-cutting design choices rather than additional forms of pluralism. We synthesize a protocol-bounded corpus of 70 works on pluralistic alignment and neighboring areas, using the objective-by-human-reference matrix to map existing approaches and identify underexplored combinations. The coded…

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• arXiv cs.CLOct 17

  Which Long Documents Matter? A Controlled Study of Data Selection for Context Extension

  Junlong Jia, Jiang Zhou, Xing Wu, Chaochen Gao Tinghao Yu, Feng Zhang +1

  Natural long documents are widely used for context extension, but which selection pipelines are effective remains unclear. We conduct a controlled comparison on Qwen2.5-3B at 128K under matched 6B-token continued-training budgets. Random sampling of natural long documents (random-long) outperforms repacking at longer evaluation lengths. Within the same candidate pool, selecting documents with high LongPPL key-token density yields further gains, unlike the evaluated generic-quality and LLM-judge selectors. We introduce truncLongPPL to reduce scoring cost by computing key-token density over a truncated prefix. At 128K, scoring the first half of the reference span yields downstream performance close to full-span scoring with a measured $2.28\times$ end-to-end scoring speedup. Across three training seeds, truncLongPPL achieves the highest mean scores among evaluated pipelines, exceeding random-long by 4.54 points on validation HELMET and 3.28 on held-out RULER, with gains most consistent in retrieval. At a 256K target context, it gains 2.82 average RULER points over random-long on Qwen2.5-3B. In short/long mixed training across Qwen2.5-3B, Qwen3-8B, and OLMo~3-7B, it gains 1.47--7.31…

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• arXiv cs.SDOct 17

  SENTRY: A Streaming Edge Network Trust Engine for Real-Time Audio Deepfake Detection

  Chirantan Banerjee, Soumita Chatterjee, Bhargab B. Bhattacharya, Hafizur Rahaman

  Real-time voice cloning has enabled large-scale streaming deepfake and vishing attacks, while existing Network Detection and Response (NDR) solutions rely on GPU-based post-processing that requires buffered audio and generalizes poorly to unseen speech synthesis models. This paper presents SENTRY, a hardware-oriented, streaming trust engine for inline deepfake detection at the network edge. SENTRY employs an integer lifting-wavelet front end together with a bank of multiplier-free transforms, including wavelet-packet envelopes, Walsh-Hadamard sequency profiles, waveform micro-statistics, and long-term spectral features, designed so that acoustic analysis needs only integer add, shift, compare and bitwise operations. A supervised gradient-boosted tree classifier is combined with an unsupervised Mahalanobis out-of-distribution detector trained solely on authentic speech, and their outputs are fused by a lightweight logistic layer to produce graduated LOG, CHALLENGE, or DROP actions. Under a closed-set protocol, SENTRY reaches 98.75% accuracy at a 1.25% equal-error rate (EER) on WaveFake, with perfect separation of HiFi-GAN and WaveGlow; its supervised track alone achieves an AUC of…

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