model releases

RealMath-Eval: Why SOTA Judges Struggle with Real Human Reasoning

arXiv:2606.10254v1 Announce Type: new Abstract: While Large Language Models (LLMs) have achieved near-perfect performance in solving high-school mathematics, their ability to evaluate the diverse reas

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Model Releases

ReasonAlloc: Hierarchical Decoding-Time KV Cache Budget Allocation for Reasoning Models

DGX agent

arXiv:2606.11164v1 Announce Type: new Abstract: Long chain-of-thought (CoT) trajectories in large language model (LLM) reasoning cause severe inference bottlenecks due to rapid key-value (KV) cache gr

model-releasesarxiv-cs-ai

10 Jun 2026

Recalling Too Well: Sycophancy Evaluation and Mitigation in Memory-Augmented Models

arXiv:2606.10949v1 Announce Type: new Abstract: Persistent memory systems promise to make LLMs more helpful by storing user beliefs over time. We show they also make models less correct by systematica

Recoverable but Not Stationary:Local Linear Structures in Weights and Activations

arXiv:2606.10929v1 Announce Type: cross Abstract: Task vectors, LoRA, activation steering, and random search around pretrained weights all suggest that learned behaviour can be controlled by linear di

RedAct: Redacting Agent Capability Traces for Procedural Skill Protection

arXiv:2606.10813v1 Announce Type: cross Abstract: Users rely on execution traces to observe agent behavior, diagnose failures, and ensure accountability. These traces contain rich procedural detail, i

ReflectiChain: Epistemic Grounding in LLM-Driven World Models for Supply Chain Resilience

arXiv:2606.10359v1 Announce Type: new Abstract: AI agents in supply chains face a fundamental epistemic gap: large language models (LLMs) interpret policies but lack physical grounding, while reinforc

Revisiting Metric Reliability for Fine-grained Evaluation of Machine Translation and Summarization in Indian Languages

arXiv:2510.07061v2 Announce Type: replace Abstract: While automatic metrics drive progress in Machine Translation (MT) and Text Summarization (TS), existing metrics have been developed and validated a

RoboGPT-R1: Enhancing Robot Task Planning with Reinforcement Learning

arXiv:2510.14828v3 Announce Type: replace Abstract: Improving the reasoning capabilities of embodied agents is crucial for robots to complete complex human instructions in long-view manipulation tasks

RobotEQ: Transitioning from Passive Intelligence to Active Intelligence in Embodied AI

model-releasesarxiv-cs-ro

arXiv:2605.06234v2 Announce Type: replace Abstract: Embodied AI is a prominent research topic in both academia and industry. Current research centers on completing tasks based on explicit user instruc

Rotate2Think: Geometric Priming via Orthogonal Rotation to Improve Language Model Reasoning

arXiv:2606.09873v1 Announce Type: cross Abstract: Reasoning models achieve strong performance on challenging tasks by generating explicit intermediate reasoning traces before producing a final answer.

SAFE: An LLM-as-Verifier Framework for Evidence-Grounded Multi-Hop Reasoning

arXiv:2604.01993v2 Announce Type: replace-cross Abstract: Multi-hop QA benchmarks often reward Large Language Models (LLMs) for spurious correctness, where models reach correct answers through invalid

Sample Where You Struggle: Sharpening Base Model Reasoning via Entropy-Guided Power Sampling

arXiv:2606.09926v1 Announce Type: cross Abstract: Sampling from the sequence-level power distribution p^alpha elicits RL-level reasoning from base language models without any parameter updates, but th

SARM2: Multi-Task Stage Aware Reward Modeling for Self Improving Robotic Manipulation

model-releasesarxiv-cs-ro

arXiv:2606.10305v1 Announce Type: new Abstract: Fine-tuning vision-language-action (VLA) policies for long-horizon manipulation still relies heavily on behavior cloning, which requires costly high-qua

SCAIL-2: Unifying Controlled Character Animation with End-to-end In-Context Conditioning

arXiv:2606.10804v1 Announce Type: new Abstract: Controlled character animation requires transferring motion from a driving sequence to a reference character. Prior works heavily rely on intermediate r

SCOPE: Sequential Causal Optimization of Process Interventions

arXiv:2512.17629v4 Announce Type: replace-cross Abstract: Prescriptive Process Monitoring (PresPM) recommends interventions during running business processes to optimize key performance indicators (KP

SHAPE: Coalition-Aware Expert Pruning for Sparse Mixture-of-Experts LLMs

arXiv:2606.09886v1 Announce Type: cross Abstract: Sparse Mixture-of-Experts (MoE) large language models achieve strong quality with low per-token compute, yet their deployment is often limited by the

SHAPO: Sharpness-Aware Policy Optimization for Safe Exploration

arXiv:2606.10228v1 Announce Type: cross Abstract: Safe exploration is a prerequisite for deploying reinforcement learning (RL) agents in safety-critical domains. In this paper, we approach safe explor

Sigma-Branch: Hierarchical Single-Path Network Reconstruction for Dynamic Inference with Reduced Active Parameters

arXiv:2606.09924v1 Announce Type: cross Abstract: Deploying deep neural networks on memory-constrained edge accelerators is bottlenecked by per-inference off-chip weight transfer rather than computati

Sim2Schedule: A Simulator-Guided LLM Framework for Autonomous Open-Pit Mine Scheduling

arXiv:2606.10286v1 Announce Type: new Abstract: Open-pit mine scheduling is a critical process for maximizing economic return under complex geotechnical and operational constraints. While Mixed-Intege

SkillResolve-Bench: Measuring and Resolving Same-Capability Ambiguity in Agent Skill Retrieval

arXiv:2606.10388v1 Announce Type: cross Abstract: Agent skill libraries are becoming routable software assets: a retrieved skill can contribute instructions, scripts, resource bindings, and execution

Small Data, Big Noise: Adversarial Training for Robust Parameter-Efficient Fine-Tuning

arXiv:2606.10610v1 Announce Type: new Abstract: Parameter-Efficient Fine-Tuning (PEFT) has become essential for adapting foundation models to downstream NLP tasks. However, current PEFT methods often

Sources: Microsoft is restricting employees from using Claude Fable 5 because of Anthropic's new 30-day data retention requirements (Tom Warren/The Verge)

Tom Warren / The Verge: Sources: Microsoft is restricting employees from using Claude Fable 5 because of Anthropic's new 30-day data retention requirements — Microsoft's legal teams are evaluating Ant

model-releasestechmeme

SPACE: Source-free Proxy Anchor Concept Erasure for MLLMs

arXiv:2606.09868v1 Announce Type: cross Abstract: As Multimodal Large Language Models (MLLMs) face growing privacy risks and regulatory constraints, machine unlearning (MU) has emerged as a crucial so

SPDM: Geometry-Modulated State Space Modeling with Manifold Constraints for Time Series Forecasting

arXiv:2606.09917v1 Announce Type: new Abstract: Multivariate time series forecasting requires capturing the continuously evolving correlation structure among interacting variables. Existing state-spac

SpineReport: Automated 3D Quantification and Reporting of Lumbar Spine Degeneration on MRI

arXiv:2606.10021v1 Announce Type: new Abstract: Lumbar spine conditions are a leading cause of disability worldwide, yet reliable quantification of degeneration from MRI remains challenging. In clinic

SSR-Merge: Subspace Signal Routing for Training-Free LoRA Merging in Diffusion Models

arXiv:2606.10617v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) merging can efficiently combine diverse generative capabilities from multiple trained LoRAs for a diffusion model. However, e

STAGE-Claw: Automated State-based Agent Benchmarking for Realistic Scenarios

arXiv:2606.10394v1 Announce Type: new Abstract: Large language models are increasingly used to power personal agents for everyday applications, but evaluating these agents remains a challenge. Existin

Streaming Knowledge Compilation: Proactive Materiality-Scored Pinning for Time-Evolving LLM Wikis

arXiv:2606.09877v1 Announce Type: cross Abstract: LLM wiki systems compile knowledge into pre-filled KV caches for efficient inference, but assume a static corpus -- an assumption that fails whenever

Structure from Reasoning, Numbers from Search: On-Premise Open LLMs as Structural Priors for Coupled MIMO Controller Tuning

arXiv:2606.11015v1 Announce Type: new Abstract: Tuning controllers for strongly coupled multi-input multi-output (MIMO) industrial processes is hard: decentralized classical auto-tuning ignores loop i

T1-Bench: Benchmarking Multi-Scenario Agents in Real-World Domains

arXiv:2606.11070v1 Announce Type: cross Abstract: Recent advances in reasoning and tool-calling capabilities of large language models (LLMs) have enabled increasingly capable agentic systems. However,

Temporal Context Conditioning for Seasonality-Aware Precipitation Nowcasting of High-Intensity Rainfall

arXiv:2606.09959v1 Announce Type: cross Abstract: Precipitation nowcasting is increasingly being approached with deep learning models that learn directly from recent radar observations. Although such

Temporal Sheaf Neural Networks with Dynamic Orthogonal Transport

arXiv:2606.10071v1 Announce Type: cross Abstract: We introduce Temporal Sheaf Neural Networks (TSNN), a temporal link prediction framework that equips each node with a time-varying orthogonal frame an

TENP: Trapezoidal Expert Neuron Pruning For Mixture-of-Experts

arXiv:2606.09885v1 Announce Type: new Abstract: Mixture-of-Experts large language models (LLMs) scale efficiently through sparse activation, yet their deployment is fundamentally constrained by the la

The 1st PortraitCraft Challenge: A CVPR 2026 Workshop Competition on Portrait Composition Understanding and Generation

arXiv:2606.10894v1 Announce Type: new Abstract: This paper presents an overview of the inaugural PortraitCraft Challenge, held as one of the official competitions at CVPR 2026. The challenge focuses o

The hyper-scaled NLP bound for maximum-entropy remote sampling

arXiv:2601.20970v3 Announce Type: replace-cross Abstract: The maximum-entropy remote sampling problem (MERSP) is to select a subset of s random variables from a set of n random variables, so as to max

The Interlocutor Effect: Why LLMs Leak More Personal Data to Agents Than Humans

arXiv:2606.09844v1 Announce Type: cross Abstract: Large Language Models (LLMs) alter their privacy behavior based on the perceived identity of their interlocutor. While safety mechanisms typically pre

The Order Matters: Sequential Fine-Tuning of LLaMA for Coherent Automated Essay Scoring

arXiv:2606.10327v1 Announce Type: new Abstract: Automated Essay Scoring (AES) systems must judge interdependent discourse elements (e.g., lead, claim, evidence, conclusion), yet most approaches treat

the paper is long (30 pages), but have your AI read it: https://arxiv.org/abs/2606.10241 here's a simple interactive tutorial on the topic t…

model-releasesyohei-nakajima--x

the paper is long (30 pages), but have your AI read it: https://arxiv.org/abs/2606.10241 here's a simple interactive tutorial on the topic that claude made: https://claude.ai/public/artifacts/038db6cf

The Shibboleth Effect: Auditing the Cross-Lingual Distributional Skew of Large Language Models

arXiv:2606.11082v1 Announce Type: new Abstract: This study investigates cross-lingual distributional skew (the Shibboleth Effect) in frontier large language models (LLMs) subjected to sustained advers

“They have been running literally the same play for seven years: scare, hype (evoking media interest), and (eventually) release. Scare, hype…

model-releasesgary-marcus--x

“They have been running literally the same play for seven years: scare, hype (evoking media interest), and (eventually) release. Scare, hype, release. And repeat. Is it that hard to see?” 👇🏼 - @GaryMa

This is a great article on how startups/frontier labs can coexist. Another way to look at this is task complexity - the number of bits of in…

model-releasesjerry-liu--x

This is a great article on how startups/frontier labs can coexist. Another way to look at this is task complexity - the number of bits of information needed to specify a task such that AI can solve th

Trainable Smooth-Rotation Transforms with Learned Channel Scales for LLM Quantization

arXiv:2606.09927v1 Announce Type: cross Abstract: Post-training quantization (PTQ) is one of the most practical ways to reduce the serving cost of Large Language Models (LLMs), but activation quantiza

Training LLMs to Enforce Multi-Level Instruction Hierarchies via Gravity-Weighted Direct Preference Optimization

arXiv:2606.10860v1 Announce Type: cross Abstract: Production LLMs receive instructions from sources with very different levels of trust, yet attend to every token with uniform architectural privilege.

TRAPS: Therapeutic Response Analysis via Pathway-informed Stratification

arXiv:2606.09898v1 Announce Type: new Abstract: Cancer treatment planning requires decisions across multiple clinical dimensions at once. Clinicians must determine whether a patient should receive tar

U-TTT: Towards Generalizable PET Image Denoising via Test-Time Training

arXiv:2606.11032v1 Announce Type: new Abstract: Existing deep learning models for Positron Emission Tomography (PET) image denoising often suffer from severe performance degradation under distribution

UMI-Bench 1.0: An Open and Reproducible Real-World Benchmark for Tabletop Robotic Manipulation with UMI Data

model-releasesarxiv-cs-ro

arXiv:2606.10382v1 Announce Type: new Abstract: Real-robot evaluation is essential for understanding whether learned manipulation policies can operate reliably outside curated demonstrations. This nee

Uncertainty-Aware Motion Planning for Autonomous Driving in Mixed Traffic Environment

arXiv:2606.09958v1 Announce Type: cross Abstract: In mixed-traffic environments where autonomous and human-driven vehicles may co-exist, motion planning for autonomous vehicles requires anticipating t

UXBench: Benchmarking User Experience in AI Assistants

arXiv:2606.09570v2 Announce Type: replace Abstract: As AI assistants serve millions of users daily, evaluating user experience (UX) beyond general model capability has become increasingly important. W