arXiv AI
Jun 10
EstRTL: Functional Estimation Guided RTL Code Generation
arXiv:2606. 09867v1 Announce Type: cross Abstract: Optimizing register transfer level (RTL) code is of vital importance in hardware design.
Large language models
Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.
arXiv AI
Jun 10
Co-GLANCE: Uncertainty-Aware Active Perception for Heterogeneous Robot Teaming
arXiv:2606. 09919v1 Announce Type: cross Abstract: Perceptual uncertainty is a central challenge for heterogeneous robot teams operating in unstructured outdoor environments, where no single viewpoint affords reliable scene understanding.
arXiv AI
Jun 10
CIAware-Bench: Benchmarking Control Intervention Awareness Across Frontier LLMs
arXiv:2606. 11063v1 Announce Type: new Abstract: AI control protocols oversee untrusted models by monitoring their actions and modifying potentially unsafe steps, often using a trusted model.
arXiv AI
Jun 10
Less Context, Better Agents: Efficient Context Engineering for Long-Horizon Tool-Using LLM Agents
arXiv:2606. 10209v1 Announce Type: new Abstract: Large language models deployed as autonomous agents for enterprise workflows face a key challenge: verbose tool responses from enterprise systems can cause context overflow, stale-state errors, and high inference cost.
arXiv AI
Jun 10
PhantomBench: Benchmarking the Non-existential Threat of Language Models
arXiv:2606. 11105v1 Announce Type: cross Abstract: Hallucinations, where language models (LMs) generate factually ungrounded responses, pose serious risks, as users tend to blindly rely on them.
arXiv AI
Jun 10
LLM-Guided Neural Architecture Search for Robust Co-Design of Physical Neural Networks
arXiv:2606. 10294v1 Announce Type: cross Abstract: Deploying neural networks on unconventional hardware demands architectures that co-optimize task accuracy and platform-specific constraints such as energy cost, physical non-idealities, and numerical precision.
arXiv Machine Learning
Jun 10
CITRAS-FM: Tiny Time Series Foundation Model for Covariate-Informed Zero-Shot Forecasting
arXiv:2606. 10798v1 Announce Type: new Abstract: Pretrained time series foundation models (TSFMs) have enabled zero-shot forecasting on unseen target series.
arXiv AI
Jun 10
ABC-Bench: An Agentic Bio-Capabilities Benchmark for Biosecurity
arXiv:2606. 11150v1 Announce Type: new Abstract: Large language models (LLMs) are rapidly acquiring capabilities relevant to biological research, from literature synthesis to interpretation of experimental data.
arXiv AI
Jun 10
Architect-Ant: Editable Automatic Furnishing of Architectural Floor Plans
arXiv:2606. 10953v1 Announce Type: new Abstract: Furnished floor plans are fundamental to real estate visualization, interior design, and architectural workflows.
arXiv AI
Jun 10
One Lens, Many Worlds : A Capability-Typed Interface for World-Model Interpretability
arXiv:2606. 09936v1 Announce Type: cross Abstract: World models are now built on substantially different computational substrates.
arXiv AI
Jun 10
What Fits (Into Few Tokens) Doesn't Overfit: Compression and Generalization in ML Research Agents
arXiv:2606. 11045v1 Announce Type: new Abstract: Reusing a held-out benchmark adaptively should, in principle, invite overfitting.
arXiv AI
Jun 10
RKSC: Reasoning-Aware KV Cache Sharing and Confident Early Exit for Multi-Step LLM Inference
arXiv:2606. 09937v1 Announce Type: cross Abstract: We introduce RKSC (Reasoning-Aware KV Cache Sharing), a training-free inference framework that eliminates two structural redundancies in multi-branch LLM reasoning pipelines.
arXiv Machine Learning
Jun 10
PRISM: Parallel Residual Iterative Sequence Model
arXiv:2602. 10796v3 Announce Type: replace Abstract: Generative sequence modeling faces a fundamental tension between the expressivity of Transformers and the efficiency of linear sequence models.
arXiv Machine Learning
Jun 10
Do Transformers Actually Help Intrusion Detection? A Temporal Sequence Evaluation on CIC-IDS2017
arXiv:2606. 11098v1 Announce Type: cross Abstract: Recent deep learning approaches for network intrusion detection increasingly incorporate temporal architectures such as recurrent networks and Transformers, often reporting near-perfect performance on CIC-IDS2017.
arXiv AI
Jun 10
Bittensor Agent Arenas as a Trajectory Primitive: Distilling a Shopping Agent from ShoppingBench Subnet Traces
arXiv:2606. 10064v1 Announce Type: cross Abstract: Small-model agentic post-training is bottlenecked less by the algorithm than by the trajectory substrate it consumes.
arXiv Machine Learning
Jun 10
Generative Archetype-Grounded Item Representations for Sequential Recommendation
arXiv:2606. 11023v1 Announce Type: cross Abstract: Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior.
arXiv AI
Jun 10
How can we assess human-agent interactions? Case studies in software agent design
arXiv:2510. 09801v3 Announce Type: replace Abstract: While benchmarks measure the accuracy of LLM-powered agents, they mostly assume full automation, failing to represent the collaborative nature of real-world use cases.
arXiv AI
Jun 10
Janus: A Benchmark for Goal-Conditioned Information Distortion in LLMs
arXiv:2606. 10852v1 Announce Type: cross Abstract: LLM deception is often evaluated through direct markers such as fabricated claims, explicit lies, or strategic concealment.
arXiv Machine Learning
Jun 10
A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models
arXiv:2606. 10277v1 Announce Type: new Abstract: Though wireless foundation models (WFMs) have shown strong potential in learning universal channel representations, their adaptation to various downstream tasks remains constrained by existing paradigms.
arXiv AI
Jun 10
Trace2Policy: From Expert Behavior Traces to Self-Evolving Decision Agents
arXiv:2606. 10457v1 Announce Type: new Abstract: Decision rules that enterprise experts apply tacitly -- in auditing, compliance, and contract review -- can be systematically recovered and improved through iterative error analysis.