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Thursday 15 October
- 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
- PaperDriveWorld3D Evidence Constrained 3D World Modeling and Generative Simulation for Real-World Autonomous Driving
Three dimensional modeling for autonomous driving must continuously estimate road geometry, obstacle occupancy, traffic participant behavior, and observational uncertaint
- 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
- PaperSCCL-DT: Semantic-Conflict-Aware Contrastive Learning with Dual-Rule Teacher Distillation for Zero-Shot Chest X-Ray Diagnosis
Contrastive vision-language pre-training supports zero-shot chest X-ray diagnosis, but standard objectives treat every off-diagonal image-report pair in a batch as a nega
- PaperBeyond the Dominant Label: Recovering Coexisting Lesion Evidence with Multiple Instance Learning
Whole-slide images may contain coexisting lesions, yet weak supervision typically provides only the most severe diagnosis. We formulate this setting as dominant-label mul
- PaperPathology foundation models for training-free case retrieval in salivary gland tumors
In pathology, pattern recognition relies on a frame of reference built through extensive training and experience, which may still fall short when encountering rare cancer
- PaperMulti-SFT: Permissively-Licensed Multilingual Instruction-Following
Supervised fine-tuning datasets remain overwhelmingly English-centric and commercially restricted, as their content is frequently generated by proprietary models whose te
- PaperLearning Changes the World and Then What Was Learned has Changed: Training and Deploying Neural Networks in the Control Loop
Neural networks offer critical adaptability for controlling complex systems where nominal modeling proves intractable. However, when offline-trained neural networks are d
- PaperLearning from Unverified Peers via Shared Residual Distillation
A language model can generate several solutions to the same problem, some leading to correct answers and others to incorrect ones. Many post-training methods turn these t
- PaperCan AI Forecast the Extremes? Evaluating Probabilistic Weather Models on Tropical Cyclones
Weather prediction is undergoing an important shift, with data-driven AI models outperforming leading numerical weather prediction systems on average at a fraction of the
- PaperRetrofitting Recurrent Vision-Language Models from Pretrained Backbones
Recent work shows that depth recurrence can be retrofitted into pretrained language models. In this work, we adapt this paradigm for visual understanding, using depth rec
- PaperThe Right Byte First: How UTF-16 Helps Multilingual Boundary Prediction
Many hierarchical byte-level language models group bytes into patches using a learned boundary predictor whose view of the next codepoint depends on encoding and lookahea
- PaperMemory as Weights: Internalizing Long-Term History for Streaming Videos
Streaming vision-language models (VLMs) must continuously integrate information from the past while remaining responsive to current observations and user queries. Existin
- PaperWhen Reasoning Is Not Enough: Proactive Inquiry for Cross-Modal Safety in Multimodal Large Language Models
Multimodal large language models (MLLMs) can act unsafely when individually benign images and texts are interpreted jointly. Beyond implicit reasoning risk, where reasoni
- PaperBeyond Context Length: Characterising the Difficulty of Long-Context Reasoning
Long-context language models support ever longer context windows, and evaluations typically treat context length as the main indicator of difficulty. However, context len
- PaperPrism-1: Position- and Rival-Independent Scoring for System One Models
Given a state and a set of options, such as intents, tools or interface elements, System One decision models return a distribution over the options in one forward pass, w
- PaperABNAT: Attention-Based Noise-Aware Training for Robust Transformers on Analog Compute-in-Memory Systems
Transformer-based models have become the backbone of a wide range of tasks, but their intensive computation and data movement incur substantial energy overhead. Analog co
- PaperLean4AgenticRL: Advancing Agentic RL with Verifiable Multi-level Process Rewards
Enhancing the agentic capabilities of Large Language Models (LLMs) to tackle complex software engineering (SWE) and general terminal-usage problems is a pressing challeng
- PaperDocumenting Trofinetide Treatment Phenotypes in Rett Syndrome with a Dual-LLM Consensus Pipeline
Objective: Trofinetide, approved in 2023 as the first Rett syndrome treatment, lacks scalable real-world monitoring because chart review is labor-intensive. We developed
- PaperFAOPs: Normal Forms and an Algebra for AI Agent Workflows
Organizations increasingly encode operational procedures as multi-step agent skills, here called Agent Operating Procedures (AOPs), that must be traceable, auditable, rep
- PaperFeaTok: Feature-Loss Tokenization for Image Generation
Discrete image generators learn the distribution of tokens produced by an image tokenizer. These token representations should be easy for generators to model, not merely
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.