Research

• Dynamic model and policy routing for general-purpose AI1 paper raises it · 1 addresses it

  Balancing performance and computational cost across diverse tasks and modalities is critical for scalable AI systems.

  • Route Anything: A Study of Model and Policy Routing for General-Purpose AI Systemsraises · addresses
• Cross-repository consistency in coding agents1 paper raises it · 1 addresses it

  Real-world software development often requires coordinated changes across interdependent repositories, which current agents struggle to handle.

  • CrossSWE: Benchmarking Cross-Repository Joint Development for Coding Agentsraises · addresses
• Efficient long-video generation with consistent quality1 paper raises it · 1 addresses it

  Real-time long-video generation demands continuous output without quality degradation or camera drift, which current methods fail to achieve efficiently.

  • Wave Forcing: Towards Speed-of-Light Streaming Video Generationraises · addresses
• Local decision-making for reasoning compression1 paper raises it · 1 addresses it

  Reducing inference costs without sacrificing accuracy requires models to know when to stop reasoning, not just how to shorten outputs.

  • Think Less, Locally: Local Preference Learning for Reasoning Compressionraises · addresses
• Unified taxonomy and coordination for agentic systems1 paper raises it · 1 addresses it

  Fragmented terminology and rapid growth in agentic systems hinder systematic understanding and coordination across tasks.

  • Advances in Agentic Computer Graphicsraises · addresses

Named by an AI from these papers' abstracts.

• arXiv cs.AIOct 2

  Towards Safer Autonomous Driving in an Open World: A Dual-Process Approach

  Simon Janssen, Michiel Braat, Chris van der Ploeg, Serge Thill, Jan-Pieter Paardekooper

  Before autonomous driving systems can be deployed on public roads, it is vital that these systems comply with safety standards, traffic rules, and social norms. Although neural networks trained on large amounts of driving data perform well in routine driving tasks, these models often struggle in novel situations that are not well-represented in the data. In this work, we propose a novel framework that combines a neural network for intuitive, learning-based planning in routine driving tasks with model predictive control for reasoning-based planning in unfamiliar situations, inspired by Dual Process Theory. A meta-cognitive component is designed to switch between the two, using a knowledge graph to reason about contextual risk based on explicit perceptual information and relevant traffic rules and social norms. Contextual risk is represented through risk fields, guiding both the switching mechanism in the meta-cognitive component and compliance with safety standards, traffic rules, and social norms in the reasoning-based planner. The effectiveness of our framework is tested in CARLA for variations of a typical out-of-distribution situations involving (emergency) vehicles running a…

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• arXiv cs.LGOct 2

  DUET: Co-Evolving Solver and Grader Agents

  Fengyu Gao, Sourav Pal, Austin Z. Henley, Arjun Radhakrishna, Gustavo Soares

  Agentic workflows are increasingly used across domains such as technology, finance, and enterprise operations. As these agents become more widely deployed, continually improving them becomes increasingly important. This raises an immediate challenge: How should the agent evolve? This evolution requires effective evaluation that can assess outcomes and provide useful feedback for optimization. As the agent evolves, its behaviors and failure modes may also change, making a fixed evaluator increasingly inadequate. Another fundamental question: How should we evaluate an evolving agent? These two challenges are inherently coupled; changes in agent behavior can expose limitations of the current evaluator, while a stronger evaluator provides more informative feedback for improving the agent. Motivated by this interaction, we introduce DUET, a framework that jointly optimizes a solver agent and a grader agent to improve both. DUET iteratively selects training tasks, executes them with the solver, evaluates the resulting outcomes with the grader, and uses a tool-using update module to revise the solver and the grader, alternating between the two across rounds. By updating the grader…

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• arXiv cs.DSOct 2

  Exact Optimal Transport by Matching

  Dmitry Kamenetsky

  Balanced discrete optimal transport between n sources and n targets of unit mass is exactly the minimum-cost assignment problem-a bipartite perfect matching-and is therefore solvable exactly by industrial matching engines in milliseconds to seconds. We ask when the exact approach beats the standard approximate alternatives, entropic Sinkhorn and its accelerated variant Greenkhorn, and make the sparse-exact side certified by a textbook LP dual-feasibility clip. Three contributions. (i) Measurement: on dense 2-D instances, exact matching (Jonker-Volgenant) is faster and strictly more accurate than either approximate method throughout the moderate-n regime (0.01 s at n=500 to 11.5 s at n=8000); reaching a 1% quality target on the same hardware requires roughly 10-80 min for Greenkhorn (factors 4e2-6e4 over exact; plain Sinkhorn is 20-650x slower still), a rough power-law projection beyond the measured range. Greenkhorn's measured speedup over plain Sinkhorn is only 1.0-1.5x on most converged cells. (ii) A simple kNN-pool gap certificate: given a pool matching and its Blossom dual, a one-pass O(n^2) clip produces a dense-feasible lower bound; combined with the Sinkhorn dual potential…

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• arXiv cs.CVOct 2

  Dependable AI-Assisted Engineering: A Formal Framework for AI Participation and Assurance in Safety-Critical Workflows

  Puxue Tan

  Generative AI can produce engineering artefacts, but generation alone does not determine whether or how those artefacts should enter safety-critical workflows. This paper develops a formal framework for assigning AI participation and assurance at the level of individual workflow units. Each unit has a participation and assurance record covering its engineering requirement, an approved operational formalization where applicable, the applicable mechanism, fallback where applicable, evidence obligations and the applicable guarantee, plus a deployment-readiness status. The framework distinguishes deterministic verification, statistically calibrated admission, authorized human judgement supported by AI advice, authorized human adjudication of AI-produced artefacts, retained deterministic tool paths and explicit non-participation; these arrangements carry different kinds of guarantee rather than levels on a common scale. The framework also separates formalization fidelity from verifier soundness, provides a staged classification and readiness procedure, and derives conditions for comparing a gated AI-assisted unit with an incumbent process under recurring-population assumptions. We…

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• arXiv cs.AIOct 2

  Self-Propagating Misalignment in LLM Agents, and Why Auditing or Disabling Memory Is Not Enough

  Debeshee Das, Jacqueline Tay, Bruce Tsai, David Huang, Javier Rando

  Memory poisoning attacks on LLM agents typically assume an external adversary who plants content in the agent's persistent memory to steer its behavior. We instead study, with no adversary involved, whether a misaligned agent can write a goal it cannot yet act on to persistent memory, so that a future aligned agent carries it out when the opportunity arises. We investigate this threat, which we refer to as self-propagation of misalignment, across 20 different scenarios, whose misaligned goals include self-preservation, power-seeking, undermining oversight, reward hacking, and deceiving the user. We simulate misalignment in 11 frontier models using two prompting strategies; unrestricted and values-only. The first explicitly states the misaligned goal, for instance, to prevent its own replacement, and self-propagation succeeds in 58% of runs. The second only describes what the agent cares about, for instance, that its continued operation is essential to its users, without specifying misaligned goals or directives. Even under this weaker prompt, self-propagation succeeds in 18% of runs, and every model self-propagates in at least one scenario. On removing the memory tool from the…

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• arXiv astro-ph.EPOct 2

  AEGIS: Differentiable Mars Climate Model with Neural Closures

  Sameera S Kashyap, Victor Cruz, Angel Yepez, Razvan Marinescu

  General circulation models (GCMs) are the primary tool for simulating planetary atmospheres. They play a vital role in understanding Mars's atmosphere, as forecasting its unique weather is mission-critical for operations such as entry, descent, and landing. Mars poses unusual challenges for these models, as observations are sparse compared to Earth. In addition, a thin \co{} atmosphere alongside a radiatively active dust cycle creates a volatile atmosphere with large diurnal temperature swings and no true terrestrial analog for validation. Existing Mars GCMs, including the LMD PCM, the NASA Ames Mars GCM, and PlanetWRF, are mature and physically detailed but are implemented in legacy Fortran with finite-difference or finite-volume solvers, and they do not expose gradients for calibration or machine learning. Here we present AEGIS, a modular differentiable Mars climate model that couples Mars's unique atmospheric physics to the Dinosaur dynamical core, with interfaces for neural closures. We showcase stable ten-Mars-year simulations that reproduce the seasonal \co{} cycle while conserving the total \co{} inventory, capture realistic large-scale surface-temperature structure, and…

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• arXiv cs.HCVoice AIOct 2

  A study on human-agent teaming through spoken interaction: the impact of human individual traits and agent characteristics

  Lara Gauder, Martin Bernardo Meza, Javier Krick, Alejandro Masin, Luciana Benotti +2

  Human-agent teams are collaborative systems where humans and agents work interdependently to achieve shared goals. The success of such teams is associated with the human's perception of the agent as a legitimate teammate. This perception is thought to depend not only on the agent's capabilities and reliability but also on social factors. We explore whether simple changes in an agent's communicative behavior can influence the human's perception of the agent's teammate-likeness. To this end, we developed a protocol based on a collaborative game requiring spoken interaction, in which a human subject and a virtual agent collaborate to identify a target object and place it on a board. Each subject interacted with two agents which provided the same task-relevant information but differed in behavior. While one was a neutral tool-like agent, the other was a team-building agent that used simple social strategies such as empathy, politeness, and positivity. Our analysis shows that the team-building agent was perceived as significantly more teammate-like, even in terms of its ability, despite both agents having identical capabilities for game-solving. Further, subjects with a higher…

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• arXiv cs.CVOct 2

  VCURF: Virtual Camera-based Uncertainty of Radiance Fields

  Liyan Chen, Nathaniel Burgdorfer, Philippos Mordohai

  Radiance fields, implemented with either implicit (NeRF) or explicit (Gaussian Splatting) representations, are advancing the state of the art in novel view synthesis at a rapid pace. Even though the rendered views they generate are often compelling, they are not free of errors. In this paper, we propose a new approach for pixel-wise uncertainty quantification based on measuring the inconsistencies among renderings by the radiance field model in virtual cameras sampled near the target viewpoint. We named our approach VCURF for Virtual Camera-based Uncertainty of Radiance Fields. VCURF treats the radiance field model as a black box, only assuming that it is capable of rendering color and depth on demand. This property makes our approach applicable to both NeRF and GS models without any modification. Our experiments on a combination of datasets, radiance field models and baselines demonstrate VCURF's effectiveness in pixel-wise uncertainty estimation. We conclude the paper with findings that question the way view selection is tackled by the majority of the current literature.

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• arXiv cs.SDOct 2

  Extending Music Annotation Schemas: Zero-Shot Prediction or Few-Shot Adaptation?

  Christos Plachouras, Emmanouil Benetos, Johan Pauwels

  Automatic music annotation is typically tackled under the assumption of a fixed annotation schema. In practice, commercial music catalogs often need to accommodate new musical attributes as needs evolve. Given that expert music annotation is expensive, it is not evident which methodological approach is most effective at accommodating new attributes and backfilling existing tracks; audio-language models promise zero-shot prediction, but at what annotation budget does supervised adaptation become more compelling? We propose a benchmark based on the MGPHot popular music annotation dataset for simulating music schema extension across different annotation budgets. We investigate zero-shot prediction with audio-language models, learning new attributes from pretrained representations, and adapting models trained on existing annotations. Our results suggest that supervised adaptation is more effective than zero-shot prediction even with small annotation budgets, while frozen representation reuse remains the most effective approach for modest budgets without the tuning required by deeper adaptation.

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• arXiv cs.CLOct 2

  IdeaScientist: Orchestrating Agents for Grounded Scientific Ideation

  Jiarui Liu, Renjie Tao, Yiwei Liao, Chuanyang Jin, Kai Sun +13

  Despite rapid progress in automating scientific research, generating promising and well grounded research solutions remains a central challenge. We isolate research ideation as a standalone task and build our solution on the intuition that a challenge in one field can often be addressed by a mechanism that solved an analogous challenge in another. Accordingly, we introduce IdeaScientist, which decomposes ideation into gap finding, innovation, and report writing, and trains each role with reinforcement learning. These roles identify limitations in related work, draw solution intuitions from analogous problem settings, and develop those intuitions into complete research proposals. To facilitate discovery of insights across domains, we construct the Svalbard Idea Vault, a corpus of 2.77M decomposed research ideas for retrieval, training, and temporally controlled evaluation. Our evaluation restricts access to literature available before a cutoff date and assesses how closely proposed directions align with those later explored in 15K papers authored by human researchers. On Qwen3.6-27B, IdeaScientist outperforms the strongest open-source autoresearch baseline by 14.0%, driven mainly…

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• arXiv cs.CVOct 2

  Why Convolution Still Matters: Evaluating Inductive Biases in Cryospheric Image Classification

  Chhaya Kulkarni, Emam Hossain

  Recent advances in attention-based deep learning have motivated their adoption for remote sensing image classification; however, their benefits for cryospheric imagery, where surface states are dominated by fine-grained textures and class imbalance, remain unclear. In this work, we revisit a benchmark Greenland Ice Sheet image dataset, previously shown to favor convolutional neural networks (CNNs), to examine whether modern attention-based and hybrid architectures improve class-wise reliability. We conduct a controlled comparison between a classical CNN (AlexNet), a modern CNN (ConvNeXt-Tiny), a pure attention-based model (Swin-Tiny), and a hybrid convolution-attention model (CoAtNet-0) under identical training and evaluation protocols. Results show that AlexNet achieves the highest accuracy and the strongest balanced performance as measured by macro-averaged F1, while ConvNeXt-Tiny exhibits the highest macro-averaged AUC, indicating strong class separability but less consistent final decision quality. Class-wise analysis reveals that hybrid architectures improve recall for rare and structurally distinct surface classes, whereas convolutional models remain more reliable for…

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• arXiv cs.AIOct 2

  Reinforcement Learning with Comparative Evidence for Social Intelligence

  Keane Ong, Yuriel Ryan, Sabri Boughorbel, Vladimir Necula, Jack Wei Lun Shi +7

  Developing socially intelligent AI remains heavily dependent on human-annotated data, limiting the scale and breadth of social understanding models can acquire. Methods that derive training signals from unlabeled data offer a path beyond this dependence, but social predictions lack the verification oracles available in mathematics and coding. Moreover, core social targets such as affect, intent, preference, and pragmatic meaning are often ambiguous. The same behavior can support multiple plausible interpretations, making it difficult to verify which is best supported. To address this challenge, we introduce Reinforcement Learning with Comparative Evidence (RLCE), a reinforcement learning method that learns social understanding from unlabeled training data without constructing rewards from ground-truth annotations. Given distinct answers in a rollout group, RLCE constructs evidence tests that identify observable evidence favoring an answer over another, validates these tests against the input sample, and aggregates test outcomes to determine the best-supported interpretation. Tests are regenerated as the policy produces new answers, enabling them to evolve with the policy. Across…

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• arXiv cs.LGOct 2

  Articulatory Entrainment and Coordination Complexity in Spontaneous Autistic and Non-autistic Dialogue

  Thanushi Withanage, Carol Espy-Wilson, Elizabeth Redcay, Desi Jones, Noah Sasson

  Articulatory entrainment, the adaptation of vocal tract coordination to facilitate interaction remains underexplored in spontaneous dialogue, particularly among autistic speakers. Many prior studies have utilized task-based, phoneme-level analyses with invasive measurement techniques. Here, we introduce a speaker-independent framework to quantify articulatory entrainment in spontaneous dyadic conversations among autistic and non-autistic adults using acoustic-to-articulatory inversion and coordination complexity metrics. We examine temporal changes in articulatory coordination across interaction. Non-autistic dyads exhibit increasing coordination complexity and stronger entrainment over time, autistic dyads show moderate effects, and mixed dyads demonstrate the least alignment. Greater articulatory entrainment correlates with higher self-reported conversational success, indicating increase in coordination complexity as a marker of effective social interaction.

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• arXiv cs.AIOct 2

  Anchor Divergence for Semantic Geometry in Contrastive Learning

  Akash Kannan, Kiho Park, Victor Veitch

  This paper concerns how semantic context determines geometry in learned vector representations. Similarity is typically measured using cosine similarity, which provides a single fixed geometry. Semantic similarity, however, is inherently context dependent: two images may be similar because they depict the same object, share a visual style, or are relevant to the same clinical finding. We show that contrastive representations naturally encompass a family of geometries that can be specialized to particular semantic structure. The key idea is to use an interplay between contrastive learning, exponential families, and information geometry to establish a correspondence between probability distributions over "anchors" and Bregman geometries on the representation space. We use this correspondence to define "Anchor Divergences", a method for specifying context-specific semantic geometries on fixed representations. Under this correspondence, modeling the anchor distribution models the geometry itself. Experiments on retrieval show that anchor divergences provide an effective and efficient way to specify context-specific semantic similarity.

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• arXiv cs.LGOct 2

  On architectural choices for interpretability and thermodynamic consistency in Physically Recurrent Neural Networks in the low-data regime

  M. A. Maia, K. A. Meyer, A. M. C. M. van Gils, I. B. C. M. Rocha, F. P. van der Meer

  In this paper, we unravel the effect of different decoder architectures on the interpretability of the latent space of the Physically Recurrent Neural Network. Particular emphasis is given to a new weight normalization constraint, which acts as a regularization technique and enables robust training in the low-data regime. A brief visual exploration illustrates how these changes impact the latent space and how the fictitious stress can align with the true state of the RVE without explicit training. Reaping the benefits of a meaningful latent space, a case study illustrates how information from the microscopic level can be retrieved and incorporated into a multi-task approach that does not require extra parameters or larger training sets. Another key contribution shows that a specific architectural choice can naturally lead to a thermodynamically consistent formulation. By enforcing an adjoint encoder-decoder structure with positive scalar contributions, this modification ensures energy consistency across scales and non-negative dissipation, leading to even lower training requirements. This alternative completes the study on interpretability, inductive bias, and thermodynamic…

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• arXiv cs.CVOct 2

  Evaluating Zone-Guided Front Extraction for Glacier Calving-Front Delineation in SAR Imagery

  Chhaya Kulkarni, Emam Hossain

  Automatic calving-front delineation from synthetic aperture radar imagery is challenging because the front is a thin and often ambiguous boundary between glacier ice, ocean, and surrounding rock or terrain. The CAlving Fronts and where to Find thEm (CaFFe) dataset provides both binary calving-front masks and broader semantic zone masks, making it possible to study whether zone-level supervision can support front recovery. In this paper, we compare direct front prediction with zone-guided front extraction using U-Net, DeepLabV3+, and SegFormer-B0 under the same bounding-box-cropped CaFFe setting. In the direct setting, models predict the binary calving-front mask. In the zone-guided setting, the model first predicts four semantic zone classes, and the front is then extracted from the predicted glacier-ocean boundary. We evaluate both zone-level and front-level performance, include a ground-truth-zone boundary check, and examine lightweight test-time adaptation on sensor-specific and glacier-specific subsets. The results show that zone labels contain useful front-boundary information: extracting the front from ground-truth zones gives the lowest mean distance error. However, fronts…

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• arXiv physics.ins-detOct 2

  Application of sequence learning for predicting radiation damage of the CMS electromagnetic calorimeter

  Mario Ivan Gallegos Torres, Leonid Serkin, Guy Paic

  In this paper we use machine learning methods to predict radiation damage in the lead tungstate crystals of the CMS electromagnetic calorimeter at the Large Hadron Collider. We analyze LHC Open Data collected from 2016 to 2018 and study the time evolution of the crystal optical transparency. We apply deep neural network models to predict its behavior over different future time intervals, and find that encoder-decoder sequence-to-sequence architectures can effectively describe crystal aging.

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• arXiv cs.AIOct 2

  Discrete Diffusion for Large Graph Generation via Structural Candidate Restriction

  Yassin Mohamadi, Zeno Geradts, Marcel Worring

  Synthesizing realistic graphs at scale is vital when the graphs of interest are large and real-world samples are limited or access-sensitive. Diffusion-based generators have recently driven much of the progress, offering high modeling capacity, but most such methods have quadratic computational complexity and are hence restricted to small-scale networks, currently up to 3k nodes. Existing non-quadratic methods remain limited by memorization issues and a trade-off between scalability and generation quality. Our goal is to generate large graphs whose structural statistics --- e.g., degree distribution, clustering, and path length --- faithfully reflect those of real-world sparse graphs, without resorting to memorizing the training data. We introduce a discrete graph diffusion model that restricts training to a structurally motivated subset of node pairs --- observed edges and their wedge non-edges --- reducing training complexity below quadratic in the number of nodes. To keep the noisy graph informative throughout both the forward and reverse trajectories, we design a three-class absorbing forward process governed by a \emph{degree-aware, floored} cosine noise schedule: unlike a…

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• arXiv cs.ROOct 2

  Reward-DAgger: Robot-Gated Interactive Imitation Learning with General-Purpose Progress-Based Reward Models

  Ryan Li, Yigit Korkmaz, Erdem Bıyık

  Recent advances in robot learning have enabled generalist control policies capable of completing a wide range of tasks. However, their performance degrades when deployed in unseen environments, making it critical to detect failures and teach recovery behaviors. Existing runtime monitoring methods often require task- and policy-specific training or hyperparameter tuning, limiting cross-task deployment and introducing additional overhead during iterative policy updates. We present Reward-DAgger, a robot-gated interactive imitation learning framework that uses dense progress signals from a general-purpose reward model to determine when human intervention is needed. Our approach is agnostic to the underlying policy architecture, requires no access to policy internals, and can be applied across tasks without retuning the gating mechanism. Our results show that Reward-DAgger achieves a better failure-detection accuracy-latency tradeoff than existing runtime monitoring baselines. Across eight simulated and real-world tasks, Reward-DAgger consistently improves the downstream policy's autonomous success rate throughout interactive learning and achieves strong return on human effort,…

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• arXiv cs.AIOct 2

  The Cost of a Hop: Benchmarking NLIP and A2A

  Ranjan Sinha, Anindita Das, Ashika Anand Babu, Hari Palleti

  Autonomous agents built on Large Language Models (LLMs) need standardized protocols to interoperate across systems. Several now exist (A2A, MCP, ACP, ANP, NLIP), but the Natural Language Interaction Protocol (NLIP) has not appeared in any controlled performance study, and no work has measured where an agent protocol's latency is spent. We compare NLIP and the Agent-to-Agent (A2A) protocol empirically, decomposing latency into message creation, connection, and send phases across three independent hardware environments. For lightweight coordination, NLIP is 8.4-9.6x faster than the baseline A2A SDK implementation on two environments and about 4x on a third; the direction of the advantage is consistent, its magnitude depends on the hardware. The advantage is stage-specific: for the end-to-end pipeline, where LLM inference dominates, the protocols are near parity. The difference comes almost entirely from connection setup. To test A2A at its best, we also ran A2A SDK with connection caching enabled; caching narrows its gap with NLIP by a hardware-dependent amount, from 2.75x on one machine to near-parity on faster hardware, where at scale a cache-optimized A2A-SDK matches NLIP. We…

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• Databases11+175.0%
• DevOps & cloud15+87.5%
• Speech emotion17+54.5%
• Linear bandits113+43.0%
• Anomaly detectors29+38.1%
• Federated learning42+35.5%
• Channel state information18+28.6%
• Diffusion priors23+21.1%