arXiv AI
Jul 7
Auto: The AGI Compiler
arXiv:2607. 04542v1 Announce Type: cross Abstract: Every LLM agent run re-derives its behavior token by token on a frontier model: brilliant, expensive, slow, and unbounded.
Benchmarks and evaluation
Leaderboards, eval harnesses and ablations — the contested business of deciding which model is actually better.
arXiv Machine Learning
Jul 7
MPINeuralODE: Multiple-Initial-Condition Physics-Informed Neural ODEs for Globally Consistent Dynamical System Learning
arXiv:2605. 13305v2 Announce Type: replace Abstract: Neural ordinary differential equations (Neural ODEs) often fit training trajectories while generalizing poorly to unseen initial conditions and long horizons.
arXiv Machine Learning
Jul 7
SMART: A Machine Learning and Monte Carlo Framework for Rapid Analysis of Stochastic Transistor Aging and Process Variation in Digital Circuits
arXiv:2607. 05187v1 Announce Type: new Abstract: As CMOS technology scales into the deep nanometer regime, digital circuit reliability is increasingly threatened by the combined stochastic effects of Bias Temperature Instability (BTI) and Process Variation (PV).
arXiv AI
Jul 7
ACPO: Adaptive Credit Policy Optimization via Fine-Grained Surrogate Entropy
arXiv:2607. 03126v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has substantially improved the reasoning ability of large language models (LLMs), but sparse outcome rewards still make token-level credit assignment difficult.
arXiv AI
Jul 7
MPSelectTune: Prompt-type Selection for Fine-tuning improves Concept Unlearning in LLMs
arXiv:2607. 03932v1 Announce Type: cross Abstract: LLMs can be conveniently adapted to a diverse set of tasks, e.
arXiv AI
Jul 7
Best-of-Better-$N$: Generating Pre-Aligned Responses with In-Context Learning
arXiv:2607. 03453v1 Announce Type: cross Abstract: Inference-time alignment methods, such as Best-of-$N$, offer a flexible alternative to training-based alignment by using reward models to select high-quality responses generated by a reference LLM.
arXiv AI
Jul 7
Lights, Camera, Carbon: Architectural Scaling Laws for Video Generation Energy Consumption
arXiv:2607. 04553v1 Announce Type: cross Abstract: We present a bidirectional framework for estimating the energy consumption of text-to-video (T2V) and text-to-video-audio (T2VA) models from architectural first principles and observable generation parameters such as resolution and duration, requiring no access to weights, model size, or implementation details.
arXiv AI
Jul 7
Context Tuning for In-Context Optimization
arXiv:2507. 04221v3 Announce Type: replace-cross Abstract: We introduce Context Tuning, a simple and effective method to significantly enhance few-shot adaptation of large language models (LLMs) without weight updates.
arXiv AI
Jul 7
A Step Towards Robust Unsupervised Domain Adaptation via Fine-Tuning and Reinforcement Learning
arXiv:2607. 03600v1 Announce Type: cross Abstract: Adversarial robustness in Unsupervised Domain Adaptation (UDA) remains a significant challenge due to noisy pseudo labels and inherent distributional shifts between the clean source and adversarially perturbed target domains.
arXiv AI
Jul 7
SILO: Simulation-in-the-Loop Sim-to-Real Transfer for Multi-Stage Cable Routing
arXiv:2607. 04616v1 Announce Type: cross Abstract: Linear-deformable manipulation remains challenging due to the complex deformations of objects such as cables and ropes.
arXiv AI
Jul 7
NouveauVoice: Generating Novel Pseudo Speakers for Voice Anonymization
arXiv:2607. 03985v1 Announce Type: cross Abstract: Advanced neural technologies in speech synthesis and voice conversion (VC) have introduced severe risks to personal privacy, necessitating robust Speaker Anonymization Systems (SAS).
arXiv AI
Jul 7
Do GUI Agents Believe Their Eyes? Diagnosing State-Belief Reliance on Pixels versus Structure
arXiv:2607. 04334v1 Announce Type: new Abstract: Multimodal GUI agents read an interface through two redundant channels: the rendered pixels of a screenshot and a serialized structure such as a DOM or accessibility tree.
arXiv AI
Jul 7
Turning Off-Policy Tokens On-Policy: A Plug-in Approach for Improving LLM Alignment
arXiv:2607. 04728v1 Announce Type: cross Abstract: Reinforcement learning (RL) post-training for large language models (LLMs) follows a efficient paradigm of "rollout then update", which inevitably results in off-policy training data.
arXiv AI
Jul 7
Forethought: Verifiable Reasoning from Neurosymbolic Primitive Programming
arXiv:2607. 04096v1 Announce Type: new Abstract: Current agentic workflows usually involve decomposing user requests into sequences of tool calls with correctly resolved parameters, the results of which are processed through reasoning traces in the language model's context window.
arXiv AI
Jul 7
Human-like Object Grouping in Self-supervised Vision Transformers
arXiv:2603. 13994v2 Announce Type: replace-cross Abstract: Vision foundation models trained with self-supervised objectives achieve strong performance across diverse tasks and exhibit emergent object segmentation properties.
arXiv AI
Jul 7
Efficient Discovery of Conditional Dependencies with Desbordante
arXiv:2607. 04030v1 Announce Type: cross Abstract: Conditional functional dependencies (CFDs) are functional dependencies with a restricted scope: they specify the context in which a dependency holds and are useful for data-quality tasks, specifying complex integrity constraints, and extracting valuable insights from data.
arXiv AI
Jul 7
Attention-Guided Efficientnet Architecture For Precise Criminal Identification in Surveillance Images
arXiv:2607. 03073v1 Announce Type: cross Abstract: Criminal identification from surveillance imagery has become a critical research area in intelligent forensic surveillance systems due to the increasing deployment of CCTV cameras in public and private environments.
arXiv AI
Jul 7
Seduced by the Narrative: Assessing Rule Adherence in Semi-Open Textual Sandboxes
arXiv:2607. 02802v1 Announce Type: cross Abstract: As LLMs are increasingly deployed as autonomous adjudicators in semi-open textual game environments, robust rule adherence becomes critical when user intent conflicts with system rules.
arXiv AI
Jul 7
K9-Bench: Evaluating Multimodal LLMs on Canine-Centric Videos
arXiv:2607. 02680v1 Announce Type: cross Abstract: MLLMs have shown strong zero-shot capabilities across diverse inputs such as across images, video, audio, and text.
arXiv AI
Jul 7
TacReasoner: A Dynamic Tactile-Language Framework for Interactive Reasoning in Real-World Scenarios
arXiv:2607. 05131v1 Announce Type: new Abstract: Among the five primary human senses, tactile is arguably the most fundamental to survival, as it enables the perception of physical contact and interaction in real-world environments.