Search: “multi-hop”

Why Prompt Optimization Works, and Why It Sometimes Doesn't: A Causal-Inspired Edit-Level Analysis

arXiv:2605.26655v1 Announce Type: new Abstract: Automated prompt optimization methods (e.g., DSpy, TextGrad) can substantially improve the performance of large language model (LLM), however, their gen

27 May 2026

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

Dynamics Reveals Structure: Challenging the Linear Propagation Assumption

DGX agent

arXiv:2601.21601v2 Announce Type: replace-cross Abstract: Neural networks adapt through first-order parameter updates, yet it remains unclear whether such updates preserve logical coherence. We invest

model-releasesarxiv-cs-ai

26 May 2026

Hardware

Inference Time Context Sparsity: Illusion or Opportunity?

arXiv:2605.24168v1 Announce Type: new Abstract: Sparsity has long been a central theme in LLM efficiency, but its role in context processing remains unresolved. As LLM workloads shift toward longer co

hardwarearxiv-cs-ai

Knowledge Graph-Driven Expert-Level Reasoning for Neuroscience

arXiv:2605.25183v1 Announce Type: cross Abstract: Knowledge graph (KG) is an abstraction that can be extracted from text corpora and used for in-depth reasoning. Prior work has leveraged KGs to fine-t

Language Models Need Sleep

arXiv:2605.26099v1 Announce Type: cross Abstract: Transformer-based large language models are increasingly used for long-horizon tasks; however, their attention mechanism scales poorly with context le

// Language Models Need Sleep // Let your agents 'sleep', folks. On a serious note, this is a fascinating paper on getting the most from lon…

// Language Models Need Sleep // Let your agents 'sleep', folks. On a serious note, this is a fascinating paper on getting the most from long-horizon agents. Here is the problem with agents today: Att

tutorialsdair-ai--x

Leveraging Spreading Activation for Improved Document Retrieval in Knowledge-Graph-Based RAG Systems

arXiv:2512.15922v3 Announce Type: replace Abstract: Despite initial successes and a variety of architectures, retrieval-augmented generation systems still struggle to reliably retrieve and connect the

tutorialsarxiv-cs-ai

Retrieval as Reasoning: Self-Evolving Agent-Native Retrieval via LLM-Wiki

arXiv:2605.25480v1 Announce Type: new Abstract: LLM agents require retrieval to behave less like one-shot context fetching and more like reasoning: searching, reading, traversing, and deciding when ev

Tool-Call Dependency Structure is Linearly Decodable in LLM Agent Residual Streams

arXiv:2605.25310v1 Announce Type: new Abstract: Tool-using LLM agents produce trajectories whose calls form a directed dependency graph: earlier tool outputs supply arguments to later calls. Whether t

Evaluating Memory Structure in LLM Agents

arXiv:2602.11243v2 Announce Type: replace-cross Abstract: Modern LLM-based agents and chat assistants rely on long-term memory frameworks to store reusable knowledge, recall user preferences, and augm

MAS-Orchestra: Understanding and Improving Multi-Agent Reasoning Through Holistic Orchestration and Controlled Benchmarks

arXiv:2601.14652v5 Announce Type: replace Abstract: While multi-agent systems (MAS) promise elevated intelligence through coordination of agents, current approaches to automatic MAS design under-deliv

Parallel Context Compaction for Long-Horizon LLM Agent Serving

arXiv:2605.23296v1 Announce Type: new Abstract: Long-horizon LLM agents accumulate growing conversation histories that eventually exceed the model's context window. Context compaction via LLM-based su

SeedER: Seed-and-Expand Retrieval from Knowledge Graphs

arXiv:2605.23753v1 Announce Type: new Abstract: Knowledge graphs (KGs) offer a rich representation for relational knowledge, but their irregular structure makes retrieval challenging: ego-graph expans

safetyarxiv-cs-lg

Ex-GraphRAG: Interpretable Evidence Routing for Graph-Augmented LLMs

arXiv:2605.21994v1 Announce Type: new Abstract: GraphRAG conditions language models on subgraphs retrieved from knowledge graphs, encoded via message-passing GNNs. Because these encoders entangle node

researcharxiv-cs-lg

23 May 2026

D3Seg: Dependency-Aware Diffusion for Brain Tumor Segmentation with Missing Modalities

arXiv:2605.22249v1 Announce Type: new Abstract: Accurate brain tumor segmentation using multiparametric MRI is critical for effective treatment planning. However, in clinical settings, complete acquis

researcharxiv-cs-cv

General Agentic Planning Through Simulative Reasoning with World Models

arXiv:2507.23773v3 Announce Type: replace-cross Abstract: What does it mean to plan? Current agentic systems, whether scaffolded workflows or end-to-end policies, rely on reactive decision-making: sel

RAGCap-Bench: Benchmarking Capabilities of LLMs in Agentic Retrieval Augmented Generation Systems

arXiv:2510.13910v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) mitigates key limitations of Large Language Models (LLMs)-such as factual errors, outdated knowledge, and hallu

Reflective Prompt Tuning through Language Model Function-Calling

arXiv:2605.21781v1 Announce Type: new Abstract: Large language models (LLMs) have become increasingly capable of following instructions and complex reasoning, making prompting a flexible interface for

Structured-Sparse Attention for Entity Tracking with Subquadratic Sequence Complexity

arXiv:2605.22476v1 Announce Type: cross Abstract: Entity tracking requires maintaining and updating latent states for entities and attributes over long sequences. Recent task-specific attention operat

local-aiarxiv-cs-cl

Agentic GraphRAG: Navigating Unstructured Financial Data with Collaborative AI

arXiv:2605.18770v1 Announce Type: cross Abstract: We present a collaborative agentic GraphRAG framework for expert analysis of commercial registry data. Public registries are often formally accessible

agentsarxiv-cs-ai

CANTANTE: Optimizing Agentic Systems via Contrastive Credit Attribution [R]

CANTANTE addresses the challenge of optimizing LLM-based multi-agent systems where system-level performance scores are available but individual agent parameters cannot be directly optimized. The frame

agentsr-machinelearning

Diagnosing Multi-step Reasoning Failures in Black-box LLMs via Stepwise Confidence Attribution

arXiv:2605.19228v1 Announce Type: cross Abstract: Large Language Models have achieved strong performance on reasoning tasks with objective answers by generating step-by-step solutions, but diagnosing

tutorialsarxiv-cs-ai

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution

arXiv:2512.05958v2 Announce Type: replace-cross Abstract: Generative search engines based on large language models (LLMs) are replacing traditional search, fundamentally changing how information provi

Query-Aware Flow Diffusion for Graph-Based RAG with Retrieval Guarantees

arXiv:2605.18775v1 Announce Type: cross Abstract: Graph-based Retrieval-Augmented Generation (RAG) systems leverage interconnected knowledge structures to capture complex relationships that flat retri

BLAgent: Agentic RAG for File-Level Bug Localization

arXiv:2605.17965v1 Announce Type: cross Abstract: Bug localization remains a key bottleneck in downstream software maintenance tasks, including root cause analysis, triage, and automated program repai

Episodic-Semantic Memory Architecture for Long-Horizon Scientific Agents

arXiv:2605.17625v1 Announce Type: new Abstract: As Large Language Models (LLMs) evolve into persistent scientific collaborators, context window saturation has emerged as a critical bottleneck. Scienti

Learning Fill-in Reduction Ordering via Graph Policy Optimization for Sparse Matrices

arXiv:2605.17362v1 Announce Type: new Abstract: Matrix reordering in large sparse solvers seeks a permutation that minimizes factorization fill-in to reduce memory and computation. Because the minimum

safetyarxiv-cs-lg

Leveraging Graph Structure in Seq2Seq Models for Knowledge Graph Link Prediction

arXiv:2605.18211v1 Announce Type: cross Abstract: We introduce Graph-Augmented Sequence-to-Sequence (GA-S2S), a novel framework that integrates a T5-small encoder-decoder with a Relational Graph Atten

MemOCR: Layout-Aware Visual Memory for Efficient Long-Horizon Reasoning

arXiv:2601.21468v5 Announce Type: replace Abstract: Long-horizon agentic reasoning necessitates effectively compressing growing interaction histories into a limited context window. Most existing memor

QuCo-RAG: Quantifying Uncertainty from the Pre-training Corpus for Dynamic Retrieval-Augmented Generation

arXiv:2512.19134v2 Announce Type: replace Abstract: Dynamic Retrieval-Augmented Generation adaptively determines when to retrieve during generation to mitigate hallucinations in large language models

Supervising the search process produces reliable and generalizable information-seeking agents

arXiv:2502.13957v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are transforming web search by shifting from document ranking to synthesizing answers, and are increasingly deplo

Beyond the Query: 5 Scenarios Laying the Foundation for the Agentic Era

model-releasesgoogle-cloud-ai

Accessing enterprise data is shifting from static reports to dynamic use by autonomous systems. To keep up, organizations must route fragmented data from SaaS, IoT, and legacy sources into secure, sca

18 May 2026

Multi-Level Contextual Token Relation Modeling for Machine-Generated Text Detection

arXiv:2605.16107v1 Announce Type: new Abstract: Machine-generated texts (MGTs) pose risks such as disinformation and phishing, underscoring the need for reliable detection. Metric-based methods, which

local-aiarxiv-cs-cl

18 May 2026

Automated Construction of a Knowledge Graph of Nuclear Fusion Energy for Effective Elicitation and Retrieval of Information

arXiv:2504.07738v3 Announce Type: replace Abstract: In this document, we discuss a multi-step approach to automated construction of a knowledge graph, for structuring and representing domain-specific

Do Reasoning LLMs Refuse What They Infer in Long Contexts?

arXiv:2602.08874v2 Announce Type: replace Abstract: Long-context LLMs can infer objectives that are not stated explicitly. This capability is useful for reasoning over documents, code, retrieved evide

safetyarxiv-cs-cl

GroupMemBench: Benchmarking LLM Agent Memory in Multi-Party Conversations

arXiv:2605.14498v1 Announce Type: new Abstract: Large Language Model (LLM) agents increasingly serve as personal assistants and workplace collaborators, where their utility depends on memory systems t

Uncovering the Representation Geometry of Minimal Cores in Overcomplete Reasoning Traces

arXiv:2605.14358v1 Announce Type: new Abstract: Language models often generate long chain-of-thought traces, but it remains unclear how much of this reasoning is necessary for preserving the final pre

CANTANTE: Optimizing Agentic Systems via Contrastive Credit Attribution

arXiv:2605.13295v1 Announce Type: cross Abstract: LLM-based multi-agent systems have demonstrated strong performance across complex real-world tasks, such as software engineering, predictive modeling,

F-GRPO: Factorized Group-Relative Policy Optimization for Unified Candidate Generation and Ranking

arXiv:2605.12995v1 Announce Type: new Abstract: Traditional retrieval pipelines optimize utility through stages of candidate retrieval and reranking, where ranking operates over a predefined candidate

safetyarxiv-cs-lg

Rethinking Graph Convolution for 2D-to-3D Hand Pose Lifting

model-releasesarxiv-cs-cv

arXiv:2605.13604v1 Announce Type: new Abstract: Graph convolutional networks (GCNs) are widely used for 3D hand pose estimation, where the hand skeleton is encoded as a fixed adjacency graph. We revis

State-Centric Decision Process

arXiv:2605.12755v1 Announce Type: new Abstract: Language environments such as web browsers, code terminals, and interactive simulations emit raw text rather than states, and provide none of the runtim

agentsarxiv-cs-ai

Combining On-Policy Optimization and Distillation for Long-Context Reasoning in Large Language Models

arXiv:2605.12227v1 Announce Type: new Abstract: Adapting large language models (LLMs) to long-context tasks requires post-training methods that remain accurate and coherent over thousands of tokens. E

safetyarxiv-cs-cl

Deep Reasoning in General Purpose Agents via Structured Meta-Cognition

arXiv:2605.11388v1 Announce Type: new Abstract: Humans intuitively solve complex problems by flexibly shifting among reasoning modes: they plan, execute, revise intermediate goals, resolve ambiguity t

From Web to Pixels: Bringing Agentic Search into Visual Perception

model-releasesarxiv-cs-cv

arXiv:2605.12497v1 Announce Type: new Abstract: Visual perception connects high-level semantic understanding to pixel-level perception, but most existing settings assume that the decisive evidence for

Geometric Factual Recall in Transformers

arXiv:2605.12426v1 Announce Type: new Abstract: How do transformer language models memorize factual associations? A common view casts internal weight matrices as associative memories over pairs of emb

ActivationReasoning: Logical Reasoning in Latent Activation Spaces

arXiv:2510.18184v3 Announce Type: replace-cross Abstract: Large language models (LLMs) excel at generating fluent text, but their internal reasoning remains opaque and difficult to control. Sparse aut

safetyarxiv-cs-ai

12 May 2026

Do Agents Need to Plan Step-by-Step? Rethinking Planning Horizon in Data-Centric Tool Calling

arXiv:2605.08477v1 Announce Type: new Abstract: Explicit planning is a critical capability for LLM-based agents solving complex data-centric tasks, which require precise tool calling over external dat

tutorialsarxiv-cs-cl

12 May 2026

Flame3D: Zero-shot Compositional Reasoning of 3D Scenes with Agentic Language Models

arXiv:2605.09218v1 Announce Type: cross Abstract: 3D scene understanding spans reasoning about free space, object grounding, hypothetical object insertions, complex geometric relationships, and integr