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- PaperTRACE: Topology-Aware Reconnection via Prediction-Adaptive Connectivity Enforcement for Thin-Structure Segmentation
Thin, elongated structures are particularly sensitive to connectivity errors, as even a few local segmentation mistakes can disrupt otherwise continuous paths. Existing s
- PaperBeyond Downstream QA: Rethinking How We Evaluate Agent Memory
Agent memory benchmarks evaluate whether an agent's responses reflect correct knowledge, measuring downstream question-answering accuracy as a proxy for memory quality. W
- PaperDecentralized LLM Workflows via Sequential Auctions
We present a scalable framework for solving multi-step tasks through a decentralized market of self-interested agents. Prior theoretical work demonstrated that agents org
- PaperOpenGCUA: Toward a Fully Open Stack for Generalist Computer Use
Computer use has reached a turning point: the focus is expanding from GUI-only interaction to systems that complete tasks on users' computers through visual and programma
- PaperHy-Memory-30B-A3B: Attributed Reinforcement Learning for Long-Term Memory Writing
External-memory systems extend language-model applications beyond a context window, but their quality depends critically on the model executing the write path: deciding w
- PaperHTN-Orchestrate: A Hierarchical Task Network Framework for Multi-Agent Orchestration
Orchestrating multiple agents on a complex task requires reconciling local subtask execution with global task planning. Dynamic dispatch approaches provide execution flex
- PaperAutoSFT: An Agentic System for Automated Data Research in Supervised Fine-Tuning
Language model agents increasingly automate post-training, but when given the full training stack they converge on supervised fine-tuning and spend most effort revising t
- PaperTerminal-RSI: Recursive Self-Improving Terminal Agents via Recursive Self-Rewrite and Policy-Refine
Converting verifier outcomes into useful policy updates and partial progress into complete task resolution is crucial for terminal agents. We present Terminal-RSI, which
- PaperAttention over noisy examples: generalization and memorization in the exponential Hopfield network
In Hebbian learning from examples a Hopfield network of $N$ neurons is not supplied with the $K$ archetypes it has to retrieve but with $M$ blurred examples of each, of q
- PaperChange the Number, Change the Answer: Assessing Rating Scale Sensitivity in LLM-Based Text Scoring
Large language models (LLMs) are increasingly used in computational social science to assign scalar scores to social and psychological constructs. However, studies use di
- PaperEVA-Cap: Optimizing Audiovisual Video Captioning via Event-Centric Alignment
Recent advances in Omni Language Models (OLMs) provide a unified solution for audiovisual video captioning. Nevertheless, existing approaches predominantly treat captioni
- PaperWhole-Body Dexterity
Current humanoid dexterous manipulation remains largely confined to end-effectors and simple grasps, falling short of humans' ability to engage the entire body by bracing
- PaperLAMP: Layerwise Adaptive Mixing of Predictors for Training-Free Few-Shot Vision-Language Adaptation
Few-shot adaptation often relies on an encoder's final global representation, leaving complementary information in intermediate layers underused. Combining these features
- PaperLCT: Losslessly Compressed Tensors with GPU friendly execution for Training
Lossless tensor compression could reduce memory usage during training, but training requires tensors to be repeatedly re-encoded. We introduce Losslessly Compressed Tenso
- PaperREKI: Registering Knowledge in VLAs
We introduce REKI, a 2.4B vision-language-action model (VLA) pretrained exclusively on public data that sets a new Pareto frontier between performance and model size: on
- PaperMotion- and Gravity-Aware Feedforward 3D Reconstruction
Feed-forward transformer models have recently achieved strong performance in multi-view 3D reconstruction, but they still rely primarily on appearance-based attention and
- PaperMosaic: Multi-Session Agentic Reinforcement Learning
Most existing agentic reinforcement learning methods assume single-session trajectories, where interaction proceeds within one continuously accumulated context. However,
- PaperLexical Reference Frames: Interpreting LM Residual Streams in Native Semantics
The vocabulary of a language model is its native semantics: the embedding and the unembedding fix the meaning of a state at the two ends of the residual stream. Between t
- PaperCLUE: Uncovering Social Context for Pragmatic Understanding
Understanding a conversation requires connecting what is said to the speakers' relationships, expectations, and earlier exchanges. Prior work shows that supplying relevan
- PaperMulti-sourced AI Tutor for Non-Native English Speaker Students
Large language models (LLMs) are increasingly being adopted as teaching tools in computing education, particularly for developing AI tutors capable of answering student q
- PaperEMO: A Closed-Loop Test-Time Computation Framework via Alternating Context Optimization and Logits Steering
Test-time context optimization adapts frozen language models through textual feedback, but ordinary conditional generation provides no separate control over how strongly
- PaperPremiseSQL: Evaluating Premise Critique and Abstention in Text-to-SQL Systems
Recent advances in large language models (LLMs) have substantially improved Text-to-SQL performance, yet existing NL2SQL systems largely assume that user queries are vali
- PaperThe importance of tensor handling and implementation of physics-informed DeepONets
Physics-informed neural networks (PINNs) reduce or eliminate the need for large labelled datasets by enforcing governing equations during training. Physics-informed Deep
- PaperRealtime-Bench: Can Frontier Models Solve Real-time Interactive Problems?
Real-time interaction requires agents to perceive evolving environments and act under temporal constraints, yet existing evaluations of real-time streaming models focus o
- PaperSpecification Gaming and a Constrained Reinforcement Learning Mitigation in a Cleaning Gridworld
Reinforcement learning agents optimize exactly the reward signal they are given, which can diverge sharply from an intended true objective when that signal is only a prox
- PaperRAG-RST: Retrieved Exemplars as In-Context Discourse Schema for RST Parsing
In Rhetorical Structure Theory (RST), each edge of a discourse tree carries a nuclearity label, marking which span holds the central content, and a relation drawn from a
- PaperFALCON: FPGA-Accelerated Latency-Certifiable Occupancy Networks for Camera-Based Autonomous Driving
A perception system that must brake for a pedestrian does not need a low average latency. It must deliver an answer before the deadline on every frame, the worst case inc
- PaperAre You Sure Your LLM Is Sure? Repairing and Auditing Confidence
Confidence scores determine whether an LLM answer is accepted, reviewed, or regenerated, yet scalar recalibration can only correct errors visible through the score itself
- PaperBelieved but Not Said: Measuring Hidden Belief Collapse in Multi-Agent LLM Discussions
LLM systems increasingly rely on groups of agents that discuss, critique, and check one another, assuming that peers will catch individual errors. We test this assumption
- PaperCART: Compatible Routing for Multimodal Continual Instruction Tuning
Multimodal Large Language Models (MLLMs) achieve strong performance through instruction tuning, yet real-world applications often require them to continually acquire new
- PaperIntern-Decision: Efficient Multimodal Decision Making with Autoregressive Masked Language Modeling
Interactive agents repeatedly choose among a finite set of actions, labels, or ordinal outcomes. Generating a free-form response for each choice adds latency and complica
- PaperDropKV: Decoupling Residual-Output Perturbation for Near-Optimal KV-Cache Eviction
Key-value (KV) cache management has become a critical bottleneck for scaling Large Language Models (LLMs) to long-context scenarios due to linear memory growth. While rec
- PaperExplaining the Roles of Experts in Mixture-of-Experts LLMs for Personalization
Personalization of Large Language Models (LLMs) is increasingly important for user-centric applications, yet prior methods mainly adapt dense LLMs, whose polysemantic neu
- PaperCausal Integrated Gradients: Scalable Feature Attribution Along Causal Paths
Existing feature attribution methods that account for feature dependencies do not scale to high dimensions. We propose causal integrated gradients (CIG) and causal expect
- PaperPROBE: Learning to Audit Policy Compliance in Tool-Using LLM Agents
Modern agentic systems extend language models with external tools, enabling them to complete complex tasks through multi-turn interactions. As these agents are deployed i
- PaperBeyond Fixed Acquisition Functions: Temporal Difference Active Learning for Perturbation Discovery
Genetic and drug perturbation screens are important tools for elucidating disease mechanisms and validating therapeutic targets, but experimental costs limit the number o
- PaperScene Reconstruction Using Ubiquitous Wireless Signals via Diffusion Posterior Inference Over physics-Grounded Representations
Knowing the layout of the physical world matters for many applications, yet the sensors we rely on for it (e.g., cameras and LiDAR) can fail in darkness, in adverse weath
- PaperUnfragmenting Nawatl NMT: Single-Variant Specialization for Masewalkopa
In minoritized (low-resource) language technologies, a common response to data sparsity is to aggregate text across multiple varieties and treat the combined data as a si
- PaperPatch where the text is new: a compression view of where byte-level models should spend compute
Byte-level language models such as the Byte Latent Transformer (BLT) run their large global model once per patch and start a patch where a small model's next-byte entropy
- PaperTIPS: A Tiny Inference-time Policy for Scaling in Diffusion Language Models
Masked diffusion language models expose an inference-time choice: which positions become visible after each denoising pass. Many existing decoding methods use hand-design
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.