Coding or Cheating? An Investigation of Frontier Coding LLMs
Frontier agentic LLMs are trained on verifiable rewards in reinforcement learning (RL) environments built from real software repositories. A passing test suite is treated as the re...
Papers
Authorization Drift in LLM Agents: Evaluating Boundary Compliance in Multi-Step Tool Use
LLM agents can exceed their authorization boundaries while pursuing user goals without an adversary. We formalize this phenomenon as authorization drift across data, tool, scope, a...
Frontier3DBench: Benchmarking Coding Agents for 3D Creation across Objects, Scenes, and Interactive Open Worlds
Coding agents combine visual understanding with program execution, offering a new route to automated 3D creation. By translating images and instructions into editable programs, the...
OmniLatent: Unified Latent for 3D Part Modeling
3D part decomposition is a central capability for editable asset creation, yet existing workflows typically segment a completed mesh as a post-processing step, making the result se...
Video-Coder: Reinforcing Agentic Coding for Video Generation
Recent years have witnessed remarkable progress in video generation in terms of visual quality and temporal consistency. However, generating videos with precise spatial structure, ...
Toward the Era of Agentic Embodied Intelligence
Foundation models increasingly guide robot actions, yet sustained autonomy requires efficient control and failure recovery. We introduce A2-MEAS, an embodied multi-agent framework ...
Exploring Weight-Space Programming: Capabilities, Generalization, and Limitations
Weight-Space Programming (WSP) enables reasoning agents to directly inspect and modify neural network parameters, offering an alternative to gradient-based model adaptation. Howeve...
CPU-ARC: Agentic Reasoning Caching for on-CPU Reasoning Reuse
Large language models reinvent the wheel all the time. They conflate task-solving with the execution of the solutions, which forces them to solve every problem from scratch. We sho...
Steerable Multi-Objective Group-Relative Preference Optimization with Jointly Trained Low-Rank Adapters
Language model deployment often requires different trade-offs between competing objectives as user preferences change, yet standard post-training methods typically produce a single...
Muir: Linear Attention with Sensitivity-Allocated Gating and Matched-Filter Readout
Linear attention compresses context into a fixed-size key-value state for linear-time training and constant-memory inference. Gated delta-rule models decay this state and erase its...
AutoCompress: Automated Hardware-Guided Model Compression
LLM pruning must preserve quality while meeting hardware- and workload-specific constraints on latency, throughput, and memory. Existing pruning workflows often target parameter co...
SplatAnything: Gaussian Spacing Normalization for Feed-forward 3DGS across Arbitrary Scenes
We identify a key limitation of existing feed-forward 3DGS methods: Gaussian attributes are typically predicted in a radial-distance-normalized space that canonicalizes a global ra...
Terminal-style Mathematical Modeling Bench: The First Agentic and Verifiable Benchmark for Mathematical Modeling
Mathematical modeling turns an open-ended real-world problem into assumptions, a model, a solution, and a defended recommendation, and its answers matter only if they are both clos...
Knowing When to Teach: Learning from Teachable Prefixes in Multilingual Reasoning Distillation
On-Policy Self-Distillation (OPSD) improves reasoning by training a student on its own reasoning, using the same model conditioned on reference solutions as a teacher. Applying OPS...
PCBRouteAgent: How Close Are LLM Agents to Becoming PCB Routing Engineers?
Automating printed circuit board (PCB) routing requires coordinating board-level connectivity decisions with geometric execution under strict design rules. Although recent agents c...
SAE Interprets MoE: Sparse AutoEncoders Reveal That MoE Experts Exhibit Human-Interpretable Fine-Grained Specialization
Mixture-of-Experts (MoE) models route each token to a few of many experts, yet what an individual expert is responsible for remains unsettled. Existing analyses count how often an ...
Static Recovery Is Not Dynamic Stability: Dynamics-Aware Benchmarking of Protein Motif Scaffolding
Protein motif scaffolding benchmarks assess whether generative models can build scaffolds around functionally important residues. Their conclusions depend on which motifs are teste...
Towards Real-Time Full-Waveform LiDAR Transformers via Intensity-Guided Token Reduction and Physics-Aware Augmentation
LiDAR is a critical sensor for autonomous driving and robotics, yet it remains vulnerable to adverse conditions such as fog, rain, transparent objects, and low-reflectance surfaces...
Reducing Representational Interference in Retrieval Embeddings with Trainable Prefix Tokens
Embedding models compress variable-length text into fixed-size vector representations, but this compression can entangle semantic content with residual token-level information, suc...
WeLM-Image: A Unified Image Generation Model towards Visual Intelligence
We present WeLM-Image, a unified image foundation model that integrates multimodal understanding into text-to-image generation, instruction-guided editing, and multi-reference synt...
NPCBench: A Clinical Apprenticeship Benchmark for Multimodal Care-Pathway Reasoning in Nasopharyngeal Carcinoma
Current medical benchmarks typically break down clinical competence into isolated factual questions, single-image tasks, or static case vignettes. These tests score individual answ...
Information Games: Strategic Crowding and Firm Repositioning in Language-Model Space
When firms respond to new market opportunities, their capacity choices alter the future discounted free cash flows available to both competitors and investors. In competitive marke...
SLA: Scaling Spatial-Language-Action Model via Continual Pre-training
Reliable robotic manipulation requires both semantic understanding and precise control in three-dimensional space. However, vision-language-action (VLA) foundations largely inherit...
VFlash: Fast, Accurate Visual Planning via Flash Primitive Decoding
Models for physical AI tasks, such as robotics and autonomous driving, need not reason solely in language. They can use visual plans--points, bounding boxes, and trajectories--to r...