Knowledge catalogue
arXiv:2609.17306v1 Announce Type: cross Abstract: Multi-agent Systems (MAS) combine multiple model outputs to solve complex reasoning tasks. However, despite rapid growth of available open-source mode
arXiv:2609.16627v1 Announce Type: new Abstract: Quantifying organizational environmental action from publicly available web content remains a challenging environmental data science problem because rel
arXiv:2609.17331v1 Announce Type: new Abstract: Large language model (LLM) agents exhibit strong language-generation and problem-solving capabilities, yet suffer from three structural limitations: per
arXiv:2609.15559v1 Announce Type: cross Abstract: Grammatical error correction (GEC) evaluation has traditionally relied on reference or edit overlap, which can penalize valid rewrites that differ fro
arXiv:2609.13692v1 Announce Type: cross Abstract: LLM serving reuses KV cache by exact prefix match, so when a prompt is assembled from a set of reusable pieces -- retrieved passages, tool definitions
arXiv:2609.11709v1 Announce Type: new Abstract: When multiple LLM agents yield conflicting answers, the decision-making process dictates whether agent diversity improves performance or merely compound
arXiv:2609.11314v1 Announce Type: new Abstract: Traffic flow prediction is a core supporting technology for intelligent transportation systems. It uses historical data to infer future traffic dynamics
arXiv:2609.09458v1 Announce Type: new Abstract: As LLM agents move from answering questions to carrying out procedures, failures can be unwarranted rather than visibly wrong: the final response looks
arXiv:2601.12723v3 Announce Type: replace-cross Abstract: Optimization benchmarks play a fundamental role in assessing algorithm performance; however, existing artificial benchmarks often fail to capt
arXiv:2609.08832v1 Announce Type: new Abstract: Large language model (LLM)-powered agents can be accurate on average yet unreliable in production, a discrepancy that has been observed but remains larg
arXiv:2609.04445v1 Announce Type: cross Abstract: A conformal certificate can be valid when an LLM answers alone and invalid when the same LLM sees peers that unanimously assert a wrong answer. The qu
arXiv:2609.03148v1 Announce Type: new Abstract: Most prior works focused on conflicts between an LLM's internal parametric knowledge and externally provided context. In contrast, we investigate how LL
arXiv:2609.02940v1 Announce Type: cross Abstract: Recent automatic speech recognition (ASR) systems increasingly integrate large language models (LLMs) to leverage their semantic knowledge, either ext
arXiv:2609.04047v1 Announce Type: cross Abstract: Background: Researchers increasingly use repeated identical prompts to audit stochastic variation in large language model (LLM) brand recommendations,
arXiv:2609.01836v1 Announce Type: cross Abstract: Long-running LLM agents rely on persistent memory to carry state across interactions, including permissions, restrictions, and revocations. When memor
arXiv:2609.02217v1 Announce Type: new Abstract: LLM agents increasingly self-improve by writing and reusing textual skills, kept either as one global document or as a flat pool of per-task entries, th
arXiv:2609.00714v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems (MAS) have shown strong potential for solving complex tasks, yet their development forces a tradeof
arXiv:2609.01431v1 Announce Type: cross Abstract: Optimal hyperparameter scaling laws describe how the best hyperparameters for large language model (LLM) training change with model and data scale, en
arXiv:2609.00384v1 Announce Type: new Abstract: Large Language Model (LLM) agents increasingly automate multi-step engineering workflows through tool use, interpretation of intermediate results, and i
arXiv:2609.00434v1 Announce Type: new Abstract: Evaluating task-oriented dialogue agents requires judging not merely whether a reply reads well but whether each turn advances the underlying workflow s
arXiv:2609.00746v1 Announce Type: new Abstract: Fine-tuning a pretrained LLM into a vision-language model (VLM) can erode the backbone's text capability, with the damage concentrated on tasks that req
arXiv:2608.30466v1 Announce Type: new Abstract: Generative Engine Optimization (GEO) is increasingly used to improve content visibility in LLM-based retrieval systems, yet its population-level effects
arXiv:2608.28615v1 Announce Type: cross Abstract: Synthetic personas based on large language models (LLMs) are increasingly proposed as substitutes for human survey respondents, yet systematic validat
arXiv:2608.30650v1 Announce Type: new Abstract: LLM agents need to sustain goal-consistent reasoning across long multi-turn interactions under strict resource constraints. However, as the multi-turn c
arXiv:2608.29605v1 Announce Type: new Abstract: Memory operations of long-horizon LLM agents are hard to supervise: an operation's value is unobservable when it is taken. But they are special -- they
This WikiSkill paper from Google is a must-read. At a high level, it shows the effectiveness of persistent agents, knowledge bases, and skills. @karpathy popularized LLM Wikis. But this paper provides
arXiv:2411.15129v3 Announce Type: replace-cross Abstract: What can we learn about language from studying how it is used by ChatGPT and other large language model (LLM)-based chatbots? In this paper, w
arXiv:2608.23834v1 Announce Type: new Abstract: The key-value (KV) cache is a primary capacity and bandwidth bottleneck in long-context LLM serving. We present Minima-KV, a retention-preserving hierar
arXiv:2608.20564v1 Announce Type: new Abstract: Multi-agent LLM systems can improve reasoning by pooling diverse perspectives, but their effectiveness depends on coordinating communication, particular
arXiv:2608.20617v1 Announce Type: new Abstract: Multi-agent LLM systems split work across models, so answering often requires knowledge that sits in another agent's context: a Sharer has encoded infor
arXiv:2608.20607v1 Announce Type: cross Abstract: Panels of inexpensive LLM judges increasingly make accept-or-escalate decisions. In factuality settings, accepting a claim because several reference-f
arXiv:2608.18952v1 Announce Type: cross Abstract: Large language models can improve recommendation quality by reasoning explicitly over user history and candidate items - for example, extracting a use
arXiv:2608.16447v1 Announce Type: new Abstract: Long-horizon embodied tasks require LLM agents to iteratively decompose high-level goals, revise plans in response to environmental feedback, and ground
arXiv:2608.14399v1 Announce Type: cross Abstract: Patients increasingly ask large language model (LLM) assistants which doctor to see, making these systems AI infomediaries: algorithms that intermedia
Friday's big release was Qwen 3.8 27B, an Apache 2 licensed 27B parameter vision-capable LLM from Alibaba's Qwen research lab. I've been looking forward to this one: 27B is an excellent size for runni
arXiv:2608.12340v1 Announce Type: new Abstract: With the rapid advancement of large language models (LLMs), generative data augmentation has attracted considerable attention for imbalanced text classi
arXiv:2608.12645v1 Announce Type: new Abstract: LLM judges have become central infrastructure for model evaluations, online grading, and reward modeling. Judges are typically validated by accuracy on
arXiv:2608.13476v1 Announce Type: new Abstract: We present Multi-Agent Reasoning and Coordination (MARC), an open-source framework that replaces monolithic LLM prompting with deterministic multi-agent
arXiv:2608.13334v1 Announce Type: new Abstract: LLM-based agents increasingly rely on external memory to support long-horizon reasoning and interaction. However, the main bottleneck is not simply stor
arXiv:2408.06849v3 Announce Type: replace Abstract: The large language model (LLM) has achieved significant success across various domains. However, the inherent complexity of causal problems and caus
arXiv:2608.10676v1 Announce Type: new Abstract: Large language model (LLM)-based search agents answer questions through multi-step interactions with external environments. However, providing complete
arXiv:2608.06871v1 Announce Type: new Abstract: Complex systems, core objects of study in artificial life, model diverse phenomena through nonlinear, feedback-driven interactions that produce emergent
arXiv:2602.20710v2 Announce Type: replace Abstract: Inspecting Chain-of-Thought reasoning is among the most common means of understanding why an LLM produced its output. But well-known problems with C
arXiv:2608.06922v1 Announce Type: new Abstract: Negotiation is a demanding social task for LLM agents, requiring strategic reasoning, persuasion, and interpersonal adaptation. Yet existing benchmarks
arXiv:2608.06909v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly operate through long-horizon trajectories involving user instructions, tool use, external observations, a
arXiv:2608.07395v1 Announce Type: cross Abstract: Large Language Model (LLM)-based automated algorithm design typically evolves algorithms as complete, indivisible programs. While this whole-program p
arXiv:2608.00004v1 Announce Type: new Abstract: Grading natural-language mathematical proofs is a recurring cost in evaluating math-reasoning systems, and frontier LLM judges are expensive. We ask whe
arXiv:2602.10429v2 Announce Type: replace-cross Abstract: AIvilization v0 is a publicly deployed large-scale artificial society that couples a resource-constrained sandbox with a unified LLM-agent arc
arXiv:2607.29334v1 Announce Type: cross Abstract: Conversational AI developed by geopolitical rivals reaches citizens worldwide, raising concerns that it could sway public opinion or be rejected as fo
arXiv:2607.29032v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly operate over long interaction histories, where effective reasoning requires identifying and exploiting
arXiv:2510.25333v2 Announce Type: replace Abstract: Recent years have witnessed the rapid development of LLM-based agents, which shed light on using language agents to solve complex real-world problem
arXiv:2607.26070v1 Announce Type: cross Abstract: Large language model (LLM)-based agentic search systems are often evaluated as if the underlying LLM were the only component that matters, yet their m
One of the papers I reviewed has what seems to be entirely LLM-generated rebuttals, and the original paper is also clearly LLM-generated, with Claude-speak everywhere. While the authors acknowledge LL
Apple ML shows LLM world-model simulation can generate API-agent training trajectories from specs alone—no executable environments required.
arXiv:2607.16900v2 Announce Type: replace Abstract: Training API-calling large language model (LLM) agents demands massive amounts of high-quality trajectories. However, collecting such data at scale
arXiv:2607.19351v1 Announce Type: new Abstract: LLM-based multi-agent systems (LLM-MAS) are increasingly deployed in safety-critical applications, where adversaries inject malicious instructions throu
arXiv:2607.12122v1 Announce Type: new Abstract: We present an agentic approach to autonomous neural operator discovery based on an AI scientific community, which consists of a swarm of virtual laborat
arXiv:2607.11919v1 Announce Type: cross Abstract: Human memory is reconstructive, not a faithful recording. Current multimodal LLMs (MLLMs) lack this capability: they process images through a frozen v
arXiv:2607.12252v1 Announce Type: new Abstract: Deep research agents are increasingly used to produce long-form financial reports, yet large-scale evaluation remains bottlenecked by the need for human
arXiv:2607.12254v1 Announce Type: new Abstract: Large language model (LLM) agents can increasingly plan, use tools, maintain memory, and execute long-horizon tasks. These advances motivate two linked