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Monday 19 October
- PaperHierarchical Token Merging with Recursive Autoencoders for Unsupervised Grammar Induction
Hierarchical abstraction is a fundamental principle across many data modalities, enabling complex sequences to be represented compositionally at multiple levels. Learning
- PaperParallel Language Models via Variational Decoding
Autoregressive language models emit one token per forward pass. This causes high inference latency, in particular as models are scaled up for quality. In this paper, we i
- PaperFoundation Model Driven Optical Flow
We investigate how frozen pretrained visual and depth representations support dense optical flow when coupled with a supervised matching network. Building on TTSFlow, thi
- PaperIntra-Rubric Reward Divergence: Mitigating Rubric Hacking through Latent Dimension Recovery and Balancing
Rubric hacking limits both performance and training robustness in rubric-based reinforcement learning. By decomposing rubrics into sub-dimensions, we reveal the Intra-Rub
- PaperM-Plan: Comprehensive Planning For LLM Agents with Memory Arrangement
Large language model (LLM)-based agents have recently demonstrated outstanding performance in single-turn tasks, yet they continue to struggle with long-horizon tasks, pa
- PaperBrushOff: Stroke-Based Re-rendering as a Watermark Removal Attack on Painting
Invisible watermarks are used to mark who owns a painting. We show that a simple attack removes the most robust one we test. The attack redraws the watermarked painting w
- PaperFrom Short Clips to Real-Time Streams: Identity-Aware Supervision and Context-Efficient Generation for Video Face Swapping
Video face swapping transfers the reference identity to a target video while preserving its other attributes and motion. Despite recent progress in reference-guided flow-
- PaperStepBench: Diagnosing Real-World Procedural Understanding in AI Agents
Evaluating an agent's planning ability remains challenging for open-domain tasks, where verifying procedures through execution is often impractical. Existing evaluations
- PaperWhich Model for Which Role? Cost-Efficient Pareto Recommendation for LLM Agentic Workflows
LLM agentic workflows increasingly employ different models for specialized roles. Users may prefer different trade-offs between task quality and deployment cost, sometime
- PaperPACT: Plan-Augmented Coordination via Trace-Graphs for Dynamic Multi-Agent Combinatorial Optimization
Multi-agent approaches have been investigated recently as a means of improving the scalability of reinforcement learning for neural combinatorial optimization. Existing m
- PaperNested Shared-Operator Recursion on ARC-AGI-1: An Empirical Study of HRM, TRM and URM
Recursive reasoning models refine persistent internal states by repeatedly applying learned operators. We investigate whether adding a persistent intermediate state that
- PaperHoneypots for AI Agent Insider Threats
AI agents deployed inside organizations can pose an insider threat if they make mistakes, are compromised by an attacker, or pursue misaligned objectives. We propose hone
- PaperCaptionFuse: Training-Free Composed Product Retrieval Via VLM Intent Captioning and Weighted Embedding Fusion
Composed Image Retrieval (CIR) retrieves products matching a reference image modified by natural language---a core capability for eCommerce search. Existing training-free
- PaperAllPath: Layered Verification for a Deployed, Self-Revising LLM Trading Agent under Human-Gated and Autonomous Regimes
LLM trading agents are evaluated almost exclusively in backtests and autonomous benchmark harnesses. Deployment, where actions are irreversible, raises a different questi
- PaperTandem: Serving Hybrid Linear-Attention GUI Agents on Mobile NPUs
Smartphone GUI agents complete tasks in mobile apps by repeatedly reading the screen and asking a model for the next action. Mobile neural processing units (NPUs) enable
- PaperSpatialScout: Can VLM Agents Actively Explore and Map 3D Scenes?
Spatial intelligence requires agents to actively acquire visual evidence and integrate observations into a coherent representation of the three-dimensional world. Yet spa
- PaperThe Development of Sycophancy in Language Models: Tracing Vulnerabilities from Pretraining to Post-Training
Language models can change a committed answer to accommodate a user's opinion or pushback without new evidence. However, when this behaviour develops during training and
- PaperThe Effectiveness and Durability of Pretraining Data Filtering in Protein Language Models
Pretraining filtering is one of the most common safeguards for open-weight language models. Here we study its effectiveness, durability and scalability using the ESM-2 fa
- PaperFrom Outcomes to Components: A Counterfactual Evaluation Framework for LLM-Proposed Repairs
A repair action proposed by a large language model (LLM) specifies which records to change and how to change their values. A failed action may still contain a useful choi
- PaperReasoning-Aware Multicalibrated Reliability Predictors for Language Models
As people increasingly rely on large language models (LLMs) for information, model providers need to communicate meaningful estimates of whether individual responses are
- PaperDual-Scale Condition-Shift Editing for Robust Real-World Image Demoiréing
Robust demoiréing of screen-recaptured images remains challenging: moiré varies in frequency, color, and spatial support across display-camera pairs and capture condition
- PaperRevision-Based Learning from Interactive Experience and Feedback
Code generation models are increasingly used to produce interactive artifacts, such as websites and user interfaces. Many properties that determine whether these interfac
- PaperWhen the harness decides success: state that cannot be re-observed
Agent harnesses decide what a language model sees across turns: they drop old turns, summarize them, or replay the model's own earlier reasoning. We ask when these choice
- PaperVaDar: Implicit Goal Conditioning via Value Disaggregation
Long-horizon tasks are important for modern AI and robotics, yet pose a challenge for reinforcement learning. Historically, a popular approach has been to decompose a tas
- PaperSleepPairNet: Learning Multimodal Pediatric Sleep Representations with Random-Pair Cross-Attention
Analyzing physiological sleep signals is important for assessing sleep quality and detecting sleep-related disorders. Learning inter-modal relationships across physiologi
- PaperEgoXperience: Learning World-Grounded Egocentric Representations from Human Experience
Reconstructing human activity from egocentric video requires more than recovering individual 3D components: the observer's motion, surrounding geometry, and hand-object i
- PaperGlobal Time-Aware Conditioning (GTC): A Mechanism for Time-Aware Pediatric Sleep Representation Learning
Sleep physiology evolves throughout the night, with changes in sleep cycles, respiratory stability, and cardiovascular dynamics. However, self-supervised learning (SSL) m
- PaperHilbert-Scale Stability of Posterior Diffusion Priors for Ill-Posed Signal Recovery
Indirect and noisy observations are difficult to invert when the forward operator smooths out the unknown signal. In this case, the unstable modes are precisely the modes
- PaperConducting Reliable Keyword Estimation with Unreliable ASR in Police Audio Data
Extracting insights from large audio data sets using human transcription is expensive and time-consuming. Off-the-shelf Automatic Speech Recognition (ASR) systems offer a
- PaperDegree Bounds for Separating Polynomial and Neural Invariants for Point Clouds
Neural networks that respect the symmetries of point clouds commonly compute high-order representations of the rotation group to improve their expressivity. Such models a
- PaperA Probabilistic, Rotation-Preserving Kernel Operator Surrogate for Continuum-Robot Design Spaces
We develop an uncertainty-aware, operator-learning-based surrogate for tendon-driven continuum robots (TDCRs) that generalizes across robot design and actuation while pre
- PaperITU-DESC: a missingness-aware machine-learning dementia risk score in the UK Biobank cohort
Background: Existing dementia risk scores rely on fixed, pre-selected features and linear models, or ignore bias from informative missingness in cohort data. Here we intr
- PaperBERT Is Already a Denoiser: Cheaply Unlocking Iterative Generation in Masked Language Models
Masked language models (MLMs) such as BERT are typically used for representation learning despite their denoising pretraining. We test whether this capability supports co
- PaperBombadil: A Foundation Model for Plant Electrophysiology
Living plants, like animals, use electrical signals to carry information about their physiological state across their body, from roots to branches. These signals can be r
- PaperConcerns in Benchmarking Jailbreaks: An Empirical Study of Attack Success Rates
Jailbreak methods are commonly compared using Attack Success Rate (ASR), but these comparisons depend on how responses are generated and judged. In this paper, we examine
- PaperBeyond Attack Success Rate: Cost-Aware Metric for Evaluating Iterative LLM Jailbreaks
Attack success rate (ASR) evaluates jailbreaks against large language models without accounting for repeated calls to the victim model. Yet each additional call gives the
- PaperEGiME: Entropy-Guided Model Evaluation in Few-Shot Learning
The feature-extraction quality of pre-trained models sets the ceiling of downstream transfer learning, yet few-shot model selection remains difficult: supervised evaluati
- PaperScaling Vision Foundation Models for Toxicologic Pathology
Here, we present TOX-FM, a family of non-clinical PFMs trained on large-scale diverse histopathology datasets of over one million whole slide images consisting of over 60
- PaperDrive2Gauss: FeedForward 4D Gaussian Reconstruction from Multimodal Generative Driving Videos
Generating reusable driving environments requires both controllable future observations and an explicit representation that supports rendering from different viewpoints,
- PaperTeaching AI Agent to Play Poker Strategically Through Preference Optimization
A poker agent can make a legal move and still make a strategically poor one. We ask whether compact AI agents can exhibit strategic behavior under hidden information and
Headlines and links come from the labs' own blogs, Hacker News and the GDELT news index, and from the sources our index already follows. We show a short excerpt and always link to the original. A launch reported by a news site rather than the lab is marked “Reported” until the lab itself announces it.