activity
20182026
most citeddyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learning

476 citations · 553 across the 11 of their papers we have counts for

collaborators

16 papers

cs.CL2026

The Answer Path and the Grounding Instruction in LLM Question Answering over Knowledge Graphs

Arquimedes Canedo

A graph retrieval-augmented generation pipeline chooses which triples to put in the prompt, a syntax to write them in, an order to write them in, and a sentence telling the model w…

cs.AI2026

Invalidation Contracts for Cross-Episode Agent Memory

Michael Wu, Arquimedes Canedo

LLM agents that cache recovery suggestions from API errors can skip re-derivation in later episodes, spending fewer tokens and fewer model calls on constraints they have already le…

cs.SE2026

Architecture as Capability Equalizer for Coding Agents

Arquimedes Canedo

LLM-based coding agents generate complete software systems from high-level descriptions, yet little is known about how the format of architecture specifications affects the quality…

cs.SE2026

Oracles That Cannot Fail: Anchoring and the Expectation That Moves With the Fault

Arquimedes Canedo

A test oracle that obtains its expected value from the system it is judging cannot fail. If a fault moves measurement and expectation together the comparison cancels exactly, and n…

cs.SE2026

Self-Reflective APIs: Structure Beats Verbosity for AI Agent Recovery

Arquimedes Canedo, Grama Chethan

When an AI agent calls an API and hits a validation error, it needs more than what went wrong -- it needs what to do next. A self-reflective API returns, on validation failure, a m…

cs.DL2026

paper.json: A Coordination Convention for LLM-Agent-Actionable Papers

Arquimedes Canedo

LLM agents routinely serve as first (and sometimes only) readers of academic papers, skimming for sub-claims, extracting reproducibility steps, and generalizing scope. Standard pro…