LLM Evolution as an Industry-Scale Ecosystem: A Lifecycle Perspective on Continual Learning
arXiv:2606.24901v1 Announce Type: new
Abstract: Continual learning capability is critical for Industrial LLMs, as deployed models must be continuously updated to meet evolving requirements and environments, rather than repeatedly retrained from scratch. However, most existing research focuses on im...
Project Auto-World: Towards Automated Benchmarking of Neural Relational Reasoners
arXiv:2606.24965v1 Announce Type: new
Abstract: Reasoning about relational structures remains a significant challenge for neural models, particularly when they must systematically apply learned knowledge to problem instances that are harder than those seen in training. Progress is hampered by the d...
Diagnosing and Mitigating Compounding Failures in Agentic Persuasion via Taxonomic Strategy Retrieval
arXiv:2606.24976v1 Announce Type: new
Abstract: Foundation-model agents in multi-step, open-ended environments frequently suffer from compounding errors, where early mistakes contaminate long-horizon trajectories. While Multi-Agent Debate (MAD) succeeds in deterministic domains, agents in subjectiv...
Do vision-language models search like humans? Reasoning tokens as a reaction-time analog in classic visual-search paradigms
arXiv:2606.25066v1 Announce Type: new
Abstract: Visual search has been one of the most productive paradigms in the study of visual attention: the way reaction time scales with the number of items distinguishes parallel, "pop-out" search from serial, attention-demanding search. I ask whether vision-...
Beyond Shapley: Efficient Computation of Asymmetric Shapley Values
arXiv:2606.25103v1 Announce Type: new
Abstract: We address the problem of explainability in machine learning models through feature attribution methods. In particular, we consider a variant of Shapley values known as Asymmetric Shapley Values (ASV), which enables the incorporation of causal knowled...
Talos: Scaling rare disease diagnosis with automated, iterative genomic reanalysis
Talos was built to help resolve a major bottleneck in genomic medicine: human review time. The open-source system recovered 90% of in-scope diagnoses while surfacing just 1.3 candidate variants per patient for expert review.
The post Talos: Scaling rare disease diagnosis with automated, iterative g...
RIFT-Bench: Dynamic Red-teaming For Agentic AI Systems
arXiv:2606.23927v1 Announce Type: new
Abstract: Agentic AI systems powered by large language models (LLMs) are rapidly evolving into autonomous decision-making systems, exposing attack vectors beyond those of traditional LLM vulnerabilities. Existing security evaluations are often tied to specific ...
Neuro-Symbolic Drive: Rule-Grounded Faithful Reasoning for Driving VLAs
arXiv:2606.23938v1 Announce Type: new
Abstract: Driving VLA models incorporating Chain-of-Thought (CoT) reasoning are attractive because they leverage pretrained VLM representations and expose intermediate decisions in natural language, yet current rationales often lack the step-by-step decision se...
arXiv:2606.23991v1 Announce Type: new
Abstract: What is an agent? What constitutes agency? With the rise of Large Language Model (LLM) systems marketed as ``coding agents'', ``AI co-scientists'', and other ``agentic" tools that promise to drive up productivity, and at the same time, ``existential" ...
Safe and Generalizable Hierarchical Multi-Agent RL via Constraint Manifold Control
arXiv:2606.24010v1 Announce Type: new
Abstract: Multi-agent systems are widely used in safety-critical applications that require coordinated behavior under strict safety constraints. Existing approaches face a fundamental trade-off: learning-based methods achieve strong empirical performance but la...
Reinforcement Learning Towards Broadly and Persistently Beneficial Models
arXiv:2606.24014v1 Announce Type: new
Abstract: As AI systems are deployed across increasingly diverse and high-stakes settings, model alignment must generalize beyond the tasks and domains seen during training. This is especially important for reinforcement learning (RL), which can introduce unexp...
Systematic Exploration of 4-Expert Heterogeneous Mixture-of-Experts via Automated Pipeline Search
arXiv:2606.23739v1 Announce Type: new
Abstract: We present an automated large-scale search pipeline for heterogeneous 4-Expert Mixture-of-Experts (MoE4) architectures within the LEMUR neural network dataset ecosystem. Building on a hand-crafted heterogeneous MoE reference model, we replace manual d...
Beyond Fixed Budgets: Characterizing the Inelasticity and Limitations of Tree-of-Thought Reasoning Strategies
arXiv:2606.20599v1 Announce Type: new
Abstract: Tree of Thought (ToT) search has become a promising direction for improving the reasoning capabilities of large language models, but deploying these methods in practice raises a question that has received little systematic attention: how do different ...
The New Associationism: Lessons from Deep Learning
arXiv:2606.20600v1 Announce Type: new
Abstract: What can the success of modern AI tell us about how humans learn? This paper argues that taking AI seriously as a model of human learning supports a modest but genuine associationism. The central finding is that supervised learning -- learning driven ...
Specifying AI-SDLC Processes: A Protocol Language for Human-Agent Boundaries
arXiv:2606.20615v1 Announce Type: new
Abstract: AI agents now participate as first-class team members across the software development lifecycle, yet no specification language exists for expressing the human-agent responsibility boundaries, approval gates, and governance constraints this collaborati...
arXiv:2606.20621v1 Announce Type: new
Abstract: Multi-agent debate improves the reliability of large language models (LLMs) through iterative peer critiques. However, fixed topologies often introduce persistent positional biases, amplify unreliable agents, and cause high sensitivity to role assignm...
Metric-Dependent Annotation Saturation for Learning from Label Distributions
When annotators disagree on a label, the disagreement itself carries signal—and the number of annotators needed to capture it depends on the evaluation metric. We fine-tune NLI models on label distributions subsampled from ChaosNLI, a dataset providing 100 independent annotator judgments per item, a...
Nine Judges, Two Effective Votes: Correlated Errors Undermine LLM Evaluation Panels
LLM-as-a-judge panels aggregate votes from multiple models, with the expectation that diverse models yield more reliable evaluations. We develop a framework to measure the true informational value of such panels and quantify how far their reliability falls short of the independent-voting ideal. Test...
From prediction to decision: Smaller models will reshape how we build AI
The bottleneck in AI systems isn't model intelligence anymore, but decision agility. Mallika Rao, engineering manager and former Netflix infrastructure lead, breaks down why smaller, task-specific models are outrunning foundational ones, and what that means for every team building at scale.
Closing the Social-Semantic Gap: SPSD for Edge-Based Prompt Compression in Cloud LLM Inference
arXiv:2606.19364v1 Announce Type: new
Abstract: The prefill stage of Large Language Model (LLM) inference is a growing contributor to cloud-scale energy cost. Many consumer-support and conversational prompts contain social scaffolding: politeness markers, apologetic preamble, repetition, and rappor...
Performance Analysis and Optimization of 3D Generative Diffusion Models across GPU Architectures
arXiv:2606.19365v1 Announce Type: new
Abstract: Diffusion models have become essential for high-fidelity 3D MRI synthesis, yet their deployment remains constrained by substantial GPU resource demands arising from hundreds of U-Net evaluations per sample and a highly heterogeneous kernel behavior. T...
Information Lattice Learning as Probabilistic Graphical Model Structure Learning
arXiv:2606.19366v1 Announce Type: new
Abstract: Information lattice learning (ILL) learns interpretable rules of a signal by alternately projecting the signal onto a partition lattice that encodes a hierarchy of abstractions and lifting selected rules back to the signal domain. When the signal is a...
Deontic Policies for Runtime Governance of Agentic AI Systems
arXiv:2606.19464v1 Announce Type: new
Abstract: Autonomous agentic AI systems driven by Large Language Models (LLMs) introduce a new class of security, privacy, and compliance challenges: an agent that can invoke tools, manipulate data, install software, and coordinate with peer agents across organ...
Measuring Curriculum Alignment across Topical Coverage, Competency, and Cognitive Depth: A Longitudinal Framework Applied to CS2013 and CS2023
arXiv:2606.19469v1 Announce Type: new
Abstract: Undergraduate computer science is governed by international curricular guidelines revised about once a decade, yet programs lack a reliable, reproducible way to measure how completely they cover the current guidelines and how that coverage shifts when...