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Building Social World Models with Large Language Models

model-releasesarxiv-cs-cl

arXiv:2606.11482v1 Announce Type: cross Abstract: Understanding and predicting how social beliefs evolve in response to events -- from policy changes to scientific breakthroughs -- remains a fundament

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By the way, public service announcement: if you're one of the numerous people posting about Anthropic's dystopian ways and you're thinking a…

DGX agent

By the way, public service announcement: if you're one of the numerous people posting about Anthropic's dystopian ways and you're thinking about getting Claude to help you write that post... don't! An

model-releasesjeremy-howard--x

11 Jun 2026

Calibrating Decision Robustness via Inverse Conformal Risk Control

arXiv:2510.07750v3 Announce Type: replace-cross Abstract: Robust optimization safeguards decisions against uncertainty by optimizing against worst-case scenarios, yet their effectiveness hinges on a p

researcharxiv-cs-lg

Calibration Drift Under Reasoning: How Chain-of-Thought Budgets Induce Overconfidence in Large Language Models

arXiv:2606.11211v1 Announce Type: cross Abstract: The ability of large language models (LLMs) to express calibrated uncertainty is important for safe deployment. Chain-of-thought (CoT) reasoning is wi

Called the LLM price wars – which are about to heat up — over two years ago. See especially predictions 3, 4, and 7, which laid out the no m…

Called the LLM price wars – which are about to heat up — over two years ago. See especially predictions 3, 4, and 7, which laid out the no moat, small profit, price war regime we have seen ever since.

safetygary-marcus--x

Can AI Agents Synthesize Scientific Conclusions?

arXiv:2606.11337v1 Announce Type: new Abstract: Scientific AI agents increasingly retrieve evidence, reason across sources, and synthesize conclusions used in consequential decisions. Yet, their abili

Can AI Reason Like an Urban Planner? Benchmarking Large Language Models Against Professional Judgment

arXiv:2606.11678v1 Announce Type: new Abstract: Problem, Research Strategy, and Findings: The rise of large language models (LLMs) raises a key question for urban planning: which forms of professional

safetyarxiv-cs-cl

Tutorials

Can confirm we saw a strong spike in growth of token consumption for Codex over last 48 hours. Unusual when we don't launch something.

tutorialsjeremy-howard--x

Can confirm we saw a strong spike in growth of token consumption for Codex over last 48 hours. Unusual when we don't launch something. Usage share of OpenAI grew vs Anthropic yesterday despite Mythos

Can News Predict the Market? Limits of Zero-Shot Financial NLP and the Role of Explainable AI

arXiv:2606.12210v1 Announce Type: new Abstract: Can financial news reliably predict short-term stock movements? Despite advances in large language models, this question remains unresolved. We revisit

researcharxiv-cs-cl

Can Open-Source LLM Agents Replace Static Application Security Testing Tools? An Empirical Assessment

arXiv:2606.11672v1 Announce Type: cross Abstract: This paper explores the value of agentic AI tools for cybersecurity purposes. We evaluate the efficacy of a general-purpose GenAI Large Language Model

local-aiarxiv-cs-ai

Capacity-Constrained Online Convex Optimization with Delayed Feedback

arXiv:2606.11711v1 Announce Type: new Abstract: Online learning with delayed feedback typically assumes that the learner can track all pending rounds until their feedback arrives. In practice, trackin

researcharxiv-cs-lg

Carbon-Aware Governance Gates: An Architecture for Sustainable GenAI Development

arXiv:2602.19718v2 Announce Type: replace-cross Abstract: The rapid adoption of Generative AI (GenAI) in the software development life cycle (SDLC) increases computational demand, which can raise the

Applications

CaReTS: A Multi-Task Framework Unifying Classification and Regression for Time Series Forecasting

arXiv:2511.09789v2 Announce Type: replace Abstract: Recent advances in deep forecasting models have achieved remarkable performance, yet most approaches still struggle to provide both accurate predict

applicationsarxiv-cs-lg

Categorical Prior Lock-in: Why In-Context Learning Fails for Structured Data

arXiv:2606.11961v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as conditional generators for structured data, relying on in-context learning (ICL) to adapt to new

Applications

Categorical Robustness Assessment for Machine Learning based Network Intrusion Detection Systems

arXiv:2606.12075v1 Announce Type: cross Abstract: Network Intrusion Detection Systems (NIDS) heavily utlize Machine Learning (ML) but ML models can be manipulated via adversarial attacks. These attack

applicationsarxiv-cs-lg

Tutorials

Causal Clothes-Invariant Feature Learning for Cloth-Changing Person Re-ID

arXiv:2305.06145v2 Announce Type: replace Abstract: In cloth-changing person re-identification (CCReID), it is critical to learn clothes-invariant feature, which can provide discriminative ID features

tutorialsarxiv-cs-cv

Causal Emotion Recognition in Conversation: Context Saturation and Discourse-Marker Evidence

arXiv:2601.00181v3 Announce Type: replace-cross Abstract: We address two persistent gaps in Emotion Recognition in Conversation: which modeling choices materially affect performance, and how recogniti

Agents

CCKS: Consensus-based Communication and Knowledge Sharing

arXiv:2606.12281v1 Announce Type: cross Abstract: In Decentralized Training and Decentralized Execution (DTDE) for cooperative Multi-Agent Reinforcement Learning (MARL), action-advising-based knowledg

agentsarxiv-cs-ai

CellNet -- Localizing Cells using Sparse and Noisy Point Annotations

arXiv:2606.12286v1 Announce Type: new Abstract: Counting living cells is an important step in many biological research workflows. Our collaborators at the Wellcome Sanger Institute study vital genes i

Certifiable Safe RLHF: Semantic Grounding and Fixed Penalty Constraint Optimization for Safer LLM Alignment

arXiv:2510.03520v2 Announce Type: replace-cross Abstract: Ensuring safety is a foundational requirement for large language models (LLMs). Achieving an appropriate balance between enhancing the utility

safetyarxiv-cs-ai

CFCamo: A Counterfactual Detect-or-Abstain Framework for Camouflaged Object Detection

model-releasesarxiv-cs-cv

arXiv:2606.11231v1 Announce Type: new Abstract: Vision-language reinforcement learning has recently shown strong target-present localization for camouflaged object detection (COD). Yet localization is

Hardware

Characterizing Software Aging in GPU-Based LLM Serving Systems

arXiv:2606.11916v1 Announce Type: cross Abstract: This paper proposes an empirical methodology to study software aging in GPU-based LLM serving systems. Traditional aging studies focus on CPU-centric

hardwarearxiv-cs-ai

CHORUS: Decentralized Multi-Embodiment Collaboration with One VLA Policy

arXiv:2606.12352v1 Announce Type: cross Abstract: Multi-robot collaboration allows robots to efficiently take on a wide range of tasks, from moving a couch through a doorway to assembling structures o

local-aiarxiv-cs-ai

Claw-SWE-Bench: A Benchmark for Evaluating OpenClaw-style Agent Harnesses on Coding Tasks

model-releasesarxiv-cs-cl

arXiv:2606.12344v1 Announce Type: cross Abstract: General-purpose agents such as OpenClaw are increasingly used as autonomous tool users, but their coding ability is difficult to measure under SWE-ben

Compatibility-Aware Dynamic Fine-Tuning for Large Language Models

arXiv:2606.11206v1 Announce Type: new Abstract: Supervised Fine-Tuning (SFT) is the predominant paradigm for aligning large language models (LLMs), yet it suffers from optimization instability and lim

safetyarxiv-cs-cl

Compiler-First State Space Duality and Portable O(1) Autoregressive Caching for Inference

arXiv:2603.09555v2 Announce Type: replace-cross Abstract: High-throughput Mamba-2 inference is usually tied to fused CUDA and Triton kernels, limiting portability across accelerator backends. We show

local-aiarxiv-cs-ai

Composing Linear Layers from Irreducibles

arXiv:2507.11688v4 Announce Type: replace Abstract: Contemporary large models often exhibit behaviors suggesting the presence of low-level primitives that compose into modules with richer functionalit

researcharxiv-cs-lg

Conformal Bayes under Label Shift: Post-Hoc Calibration vs. In-Training Adaptation

model-releasesarxiv-cs-lg

arXiv:2606.11865v1 Announce Type: cross Abstract: Conformal Bayes combines Bayesian posterior predictives with conformal calibration to produce prediction sets that are both statistically valid and ge

Consensus-based optimization (CBO): Towards Global Optimality in Robotics

arXiv:2602.06868v2 Announce Type: replace Abstract: Zero-order optimization has recently received significant attention for designing optimal trajectories and policies for robotic systems. However, mo

researcharxiv-cs-ro

ConsistencyPlanner: Real-time Planning with Fast-Sampling Consistency Models

arXiv:2606.11569v1 Announce Type: cross Abstract: Closed-loop planning in complex, real-world driving scenarios presents a critical challenge for autonomous driving systems. While traditional rule-bas

safetyarxiv-cs-ai

Applications

Contactless 3D Human Body Measurement Using Depth Cameras for Smart Health Monitoring

arXiv:2606.11578v1 Announce Type: new Abstract: Contactless body measurement technologies are becoming increasingly significant for smart health monitoring, digital health applications, and remote pat

applicationsarxiv-cs-cv

Context-Aware Multimodal Claim Verification in Spoken Dialogues

model-releasesarxiv-cs-cl

arXiv:2606.11420v1 Announce Type: new Abstract: Every day, millions absorb claims from podcasts and streams that no fact-checker ever sees. Spoken misinformation is built through conversation, where c

Context-Driven Incremental Compression for Multi-Turn Dialogue Generation

arXiv:2606.12411v1 Announce Type: new Abstract: Modern conversational agents condition on an ever-growing dialogue history at each turn, incurring redundant attention and encoding costs that grow with

researcharxiv-cs-cl

Continual Learning with Support Boundary Experience Blending

arXiv:2507.23534v3 Announce Type: replace-cross Abstract: Continual learning (CL) seeks to mitigate catastrophic forgetting when models are trained with sequential tasks. A common approach, experience

Corpus Augmentation for Sign Language Translation via LLM-Guided Video Stitching

arXiv:2606.11925v1 Announce Type: new Abstract: Sign language translation (SLT) converts sign language video into spoken language text and holds significant promise for improving accessibility and ena

safetyarxiv-cs-cv

Agents

Counterexample Guided Learning in the Large using Reasoning Agents

arXiv:2606.11521v1 Announce Type: new Abstract: LLMs and LLM agents should improve when given feedback, but identifying when they are able to do so is difficult: feedback is heterogeneous, domain-spec

agentsarxiv-cs-lg

CountZES: Counting via Zero-Shot Exemplar Selection

arXiv:2512.16415v3 Announce Type: replace Abstract: Object counting in complex scenes is particularly challenging in the zero-shot (ZS) setting, where instances of unseen categories are counted using

CoVar: Confidence-Variance-Guided Pseudo-Label Selection for Semi-Supervised Learning

arXiv:2601.11670v3 Announce Type: replace-cross Abstract: Pseudo-label selection in semi-supervised learning is commonly driven by maximum-confidence thresholds, yet confidence alone can be unreliable

Coverage Guarantees for Pseudo-Calibrated Conformal Prediction under Distribution Shift

model-releasesarxiv-cs-lg

arXiv:2602.14913v2 Announce Type: replace Abstract: Conformal prediction (CP) offers distribution-free marginal coverage guarantees under an exchangeability assumption, but these guarantees can fail i

CoVR-R:Reason-Aware Composed Video Retrieval

model-releasesarxiv-cs-cv

arXiv:2603.20190v2 Announce Type: replace Abstract: Composed Video Retrieval (CoVR) aims to find a target video given a reference video and a textual modification. Prior work assumes the modification

CP4SBI: Local Conformal Calibration of Credible Sets in Simulation-Based Inference

arXiv:2508.17077v3 Announce Type: replace-cross Abstract: Current experimental scientists have been increasingly relying on simulation-based inference (SBI) to invert complex non-linear models with in

local-aiarxiv-cs-lg

Tutorials

CredibleDFGO: Differentiable Factor Graph Optimization with Credibility Supervision

arXiv:2605.06100v2 Announce Type: replace-cross Abstract: Global navigation satellite system (GNSS) positioning is widely used for urban navigation, but the covariance reported by the GNSS solver is o

tutorialsarxiv-cs-ai

Critic Architecture Matters: Dual vs. Unified Critics for Humanoid Loco-Manipulation

arXiv:2606.11891v1 Announce Type: cross Abstract: Multi-objective reinforcement learning for humanoid robots must coordinate locomotion and manipulation within a single policy. A natural design choice

safetyarxiv-cs-lg

Cross-Domain Multi-Person Human Activity Recognition via Near-Field Wi-Fi Sensing

arXiv:2510.17816v2 Announce Type: replace-cross Abstract: Wi-Fi-based human activity recognition (HAR) provides substantial convenience and has emerged as a thriving research field, yet the coarse spa

Cross-Layer Discrete Concept Discovery for Interpreting Language Models

arXiv:2506.20040v3 Announce Type: replace-cross Abstract: Interpreting language models remains challenging due to the existence of residual stream, which linearly mixes and duplicates features across

Cross-Modal Benchmarking for Robotic Perception in Natural Environments

model-releasesarxiv-cs-cv

arXiv:2606.11563v1 Announce Type: new Abstract: Natural environments present a complex challenge to robotics perception systems. Current models, particularly vision foundation models, are largely trai

CRUMB: Efficient Prior Fitted Network Inference via Distributionally Matched Context Batching

arXiv:2606.11473v1 Announce Type: cross Abstract: Prior-fitted networks (PFNs) are a promising class of tabular foundation models that perform in-context learning, whereby the entire labelled training

CU-Multi: A Dataset for Multi-Robot Collaborative Perception

arXiv:2509.19463v2 Announce Type: replace Abstract: A central challenge for multi-robot systems is fusing independently gathered perception data into a unified representation. Despite progress in Coll

researcharxiv-cs-ro