LoKiFormer: Locality-aware Attention with Decoupled Knowledge Memory for Efficient Large Language Model Pretraining
arXiv:2608.12419v1 Announce Type: new
Abstract: Large language models (LLMs) have achieved remarkable breakthroughs across various applications. However, their architectures remain inefficient in pretraining due to two main limitations: (i) self-attention lacks an explicit inductive bias for locali...
Nobody wants another chatbot: 7 interface architectures worth building
Chat windows were always the entry point to agentic AI rather than the destination. Here are the seven interface architectures, canvases, generative UI, MCP Apps, activity panels, approval checkpoints, agent debuggers, and ambient surfaces that production teams are actually building in 2026.
Poor Man's Agentic Modeling: Simulating Large LLM-Agent Societies on a Laptop
arXiv:2608.11215v1 Announce Type: new
Abstract: Simulating societies of many large language model (LLM) agents is expensive, yet the questions asked of such simulations are usually macroscopic: phase behaviour, stylised facts, and scaling with the number of agents $N$, not the cognition of any sing...
A Forced-Structure Reduction and Verifiable Bounds for Conway's 99-Graph
arXiv:2608.11211v1 Announce Type: new
Abstract: Conway's 99-graph problem asks whether a strongly regular graph with parameters $\mathrm{srg}(99,14,1,2)$ exists. We report a systematic, fully reproducible attack by an autonomous AI research agent, scored under the track's partial-credit metric. Our...
Distribird: Literature-Informed Prior Distribution Design for Bayesian Model Calibration
arXiv:2608.11210v1 Announce Type: new
Abstract: Bayesian calibration of process-based models requires a prior distribution for each model parameter. Despite decades of methodological work, researchers almost always fall back on uniform priors. The main reason is that building informative priors fro...
Dynamic Governance of Multi-LLM Agent Systems for Collaborative Conversational Outcomes
arXiv:2608.11207v1 Announce Type: new
Abstract: When two LLM agents with structurally opposed objectives interact across multiple turns, the absence of a shared goal function produces not competition but collapse: the visitor capitulates, the site agent stops varying its approach, and the conversat...
Federated Learning for Distributed CNC Tool Wear Prediction
arXiv:2608.11281v1 Announce Type: new
Abstract: Tool wear prediction is an important task in CNC machining, where accurate monitoring of tool condition supports product quality and process reliability. Machine learning methods have shown potential for this task, but their use in industrial environm...
Basin: Efficient and Extensible Numerical Optimization in Rust
arXiv:2608.11279v1 Announce Type: new
Abstract: Basin is a numerical optimization library for the Rust programming language. Numerical optimization is the task of finding the inputs that minimize a function, and it is a fundamental element across the sciences: fitting a model to data, calibrating a...
FarSky: Task-Aware Latent-Space Coupling for Generative Intra-Hour Solar Forecasting
arXiv:2608.11254v1 Announce Type: new
Abstract: Accurate solar irradiance forecasting is essential for the reliable integration of photovoltaic power into modern electricity grids. All-sky imagers (ASI) provide high-resolution observations of clouds, making them well suited for intra-hour forecasti...
ReCBM: Uncertainty-Gated Relational Reasoning for Concept Bottleneck Models
arXiv:2608.10004v1 Announce Type: new
Abstract: Concept Bottleneck Models (CBMs) provide an interpretable framework by grounding predictions in human-understandable concepts, enabling semantic inspection and test-time intervention. Recent variants have improved CBMs through richer concept represent...
SPOTting the Future: Lookahead Explanations for Deep Reinforcement Learning
arXiv:2608.09967v1 Announce Type: new
Abstract: Deep reinforcement learning (DRL) agents achieve strong performance in complex environments, yet their decision-making processes remain difficult to interpret. We introduce SPOT (Sampling Policy Observation Tree), a novel model-agnostic, sampling-base...
arXiv:2608.09949v1 Announce Type: new
Abstract: This study evaluates the application of Large Language Models (LLMs) in complex biological systems, evolving from data analysis to autonomous, AI-guided experimentation. The framework is driven by data from a 49-channel phytosensor network, encompassi...
DOCSCHISEL: Adaptive Tool Documentation Optimization Framework for LLM Agents
arXiv:2608.10037v1 Announce Type: new
Abstract: Large language models (LLMs) increasingly rely on external tools to accomplish complex real-world tasks, making tool documentation a critical grounding resource for LLM agents. Existing studies mainly focus on improving the tool-use capabilities of LL...
arXiv:2608.10016v1 Announce Type: new
Abstract: Heterogeneous federated systems require agents to learn and exchange informative representations despite differences in data distributions, sensing modalities, model architectures, latent dimensionalities, and local learning objectives. To address thi...
CurveFP: Rational-Radix Logarithmic Datatypes with Closed Products for Language Models
arXiv:2608.10010v1 Announce Type: new
Abstract: Low-precision datatypes reduce language-model cost, but most formats optimize scalar fidelity while leaving the arithmetic induced by their products unchanged. We introduce CurveFP, a closed-product codebook family that distributes quantized magnitude...
Introducing CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurement
Radiology AI is evolving beyond report generation. CARE-X explores a unified approach that combines flexible reasoning, calibrated predictions, and measurement-based tools for chest X-ray interpretation.
The post Introducing CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision...
Training Variable Long Sequences with Data-Centric Parallel
arXiv:2608.07524v1 Announce Type: new
Abstract: Training deep learning models on variable long sequences poses significant computational challenges. Existing methods force a difficult trade-off between efficiency and ease-of-use. Simple approaches use static configurations that cause workload imbal...
Emotion in an active inference model of human driving
arXiv:2608.07480v1 Announce Type: new
Abstract: Active inference has emerged as a principled framework for modeling adaptive behavior by balancing goal-directed action with uncertainty reduction. It has been successfully applied across biological and artificial systems, including recent work on hum...
Determinization in Structure Theories: A Unified Framework via Closure, Comparability, and Joint Admissibility
arXiv:2608.07476v1 Announce Type: new
Abstract: We develop a formal framework for constructing canonical interpretations from plural structure theories. A structure theory is a triple T = ({\Sigma}, A, I) consisting of a signature, axioms, and an inference policy, whose admissible interpretation fa...
SkillConsist: Detecting Inconsistencies in Agent Skills via Bidirectional Graph Alignment
arXiv:2608.07639v1 Announce Type: new
Abstract: Agent Skills provide reusable capabilities to LLM agents. Agent Skill inconsistencies can expose undisclosed dangerous behavior or cause wrong Skill selection. Recent Agent Skill research has increasingly examined Agent Skill consistency detection. Ex...