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# Paper Corpus Summary: 2025-07-08 to 2026-07-08
status: generated-summary
## Scope
- source: arXiv API
- window: 2025-07-08 to 2026-07-08
- query groups: LLM agent, language agent, AI agent, agentic AI, evaluation, memory, tool use, coding agent, web/GUI/computer-use, multi-agent LLM, safety, RAG, planning, autonomous agent
- unique candidates seen: 1506
- high-relevance candidates selected into `papers/items/`: 970
- total paper items after expansion: 974
## Data Files
- full candidate manifest: `data/arxiv-agent-candidates-2025-07-08-to-2026-07-08.json`
- high-relevance paper manifest: `data/arxiv-agent-papers-2025-07-08-to-2026-07-08.json`
- full knowledge index: `data/index.json`
- summary index: `data/summary.json`
## Selected Corpus Distribution
### By Month
| Month | Count |
| --- | ---: |
| 2025-07 | 2 |
| 2025-08 | 4 |
| 2025-09 | 8 |
| 2025-10 | 8 |
| 2025-11 | 4 |
| 2025-12 | 5 |
| 2026-01 | 6 |
| 2026-02 | 28 |
| 2026-03 | 29 |
| 2026-04 | 48 |
| 2026-05 | 181 |
| 2026-06 | 490 |
| 2026-07 | 157 |
This distribution is partly query and recency biased, but the May-July 2026 density still strongly suggests rapid acceleration in Agent papers. The full candidate pool shows the same shape: 249 in 2026-05, 785 in 2026-06, and 265 in 2026-07.
### By Primary Query Group
| Query Group | Selected Count |
| --- | ---: |
| agent-memory | 103 |
| agent-safety | 96 |
| agent-evaluation | 93 |
| planning-agent | 77 |
| language-agent | 71 |
| agentic-ai | 70 |
| function-calling | 66 |
| autonomous-agent-llm | 63 |
| multi-agent-llm | 63 |
| llm-agent | 62 |
| rag-agent | 46 |
| coding-agent | 45 |
| ai-agent | 41 |
| tool-use | 37 |
| web-gui-agent | 37 |
### By Topic Tag
Auto tags are intentionally broad and should be treated as recall-oriented.
| Topic | Count |
| --- | ---: |
| agent-evaluation | 831 |
| tool-use | 727 |
| rag | 462 |
| reasoning | 379 |
| planning | 348 |
| agent-safety | 343 |
| memory | 292 |
| computer-use | 256 |
| multi-agent | 245 |
| workflow-agent | 236 |
| coding-agent | 202 |
| world-model | 82 |
| embodied-agent | 66 |
## Trend Readout
### 1. Evaluation Is the Center of Gravity
The strongest corpus signal is not a single Agent architecture. It is evaluation: benchmarks, trajectory auditing, failure diagnosis, safety testing, harness effects, and long-horizon task scoring.
Implication: our knowledge base needs an `agent-evaluation` track that covers final-state success, trajectory-level correctness, failure localization, cost/latency, user interaction quality, and safety.
### 2. Memory Became a Systems Problem
The memory cluster is large and diverse: active memory, selective retention, memory poisoning, memory governance, GUI memory, cross-episode memory, procedural memory, and long-horizon state management.
Implication: treat memory as an architecture subsystem with write policy, read policy, retention, forgetting, provenance, safety, and evaluation. Do not reduce it to vector search.
### 3. Safety Shifted From Refusal to Runtime Risk
Agent safety papers now focus on tool-use leakage, data injection, prompt injection, memory poisoning, action-boundary violations, governance, and runtime controls.
Implication: safety docs should cover permissions, sandboxing, irreversible action confirmation, tool schemas, provenance, and runtime intervention.
### 4. Coding Agents Are Moving Beyond SWE-Bench
The coding-agent cluster includes long-horizon maintenance, repository-level coding, compiler optimization, GUI/terminal agents, action-boundary violations, code memory, and benchmark variants.
Implication: coding-agent evaluation should include repository exploration, multi-file edits, release-note implementation, dialogue clarification, build/test loops, and failure attribution.
### 5. Computer-Use and GUI Agents Are Becoming Their Own Layer
The corpus includes OS/macOS/GUI/mobile/browser/computer-use benchmarks, visual state comparison, task-state representation, and scientific instrument control.
Implication: computer-use agents need their own tool taxonomy, safety checklist, and benchmark map.
### 6. Multi-Agent Is Splitting Into Collaboration, Governance, and Shared Memory
The multi-agent cluster is not only debate or role play. It includes shared memory governance, orchestration, role specialization, majority-vote failure, routing, and organizational behavior.
Implication: multi-agent notes should separate collaboration protocols, shared state, security boundaries, and evaluation.
## Immediate Reading Priorities
P0 clusters:
- Agent evaluation / benchmark methodology
- Memory architecture and memory evaluation
- Agent safety / runtime governance
- Coding agent long-horizon evaluation
- Computer-use / GUI agent benchmarks
P1 clusters:
- Multi-agent shared memory and orchestration
- RAG-agent and technical literature agents
- Domain-specific agents in healthcare, finance, energy, cybersecurity
- Embodied/world-model agents
## Caveats
- This is a high-recall automated arXiv sweep, not a curated bibliography.
- Auto tags are broad and may over-tag papers whose abstracts use common words like planning, action, retrieval, or benchmark.
- The selected 970 are ordered by automated collection score, not by paper quality.
- The full 1506-candidate manifest should be used as the reserve pool for future expansion.