Excited to attend #ACL2026, #ICML2026 & #SIGIR2026 this July! 🎉
Presenting 3 papers at ACL & ICML and organizing the AgentSearch Workshop at SIGIR.
If you’re attending too, let’s connect! Looking forward to meeting you there.
I’m a PhD student at UCL @uclcs , affiliated with the UCL NLP Group @ucl_nlp and the UCL Centre for AI @ai_ucl .
My research focuses on LLM agents, orchestration systems, agent evaluation & diagnosis, and agent/tool search. Find more on my personal website.
@emine_yilm@qzhang_cs@QSFByte Also excited about:
“InnoEval: On Research Idea Evaluation as a Knowledge-Grounded, Multi-Perspective Reasoning Problem”
https://t.co/SRKYTfNCk1
#NLP#AIResearch
🚀 New paper: AgentSearchBench — a benchmark for AI agent search in the wild.
As agent ecosystems grow, a fundamental question emerges: how do we find the right agent for a task?
We build a benchmark with ~10k real-world agents and show:
• Semantic similarity ≠ agent performance
• Description-based ranking often underestimates capable agents
• Execution-aware probing improves ranking
📄 Paper: https://t.co/lXJjpqTvCM
Joint work with Arastun Mammadli @arastunmammadli (co-first author) and Xiaoyu Zhang, Emine Yilmaz @emine_yilm at the UCL AI Center @ai_ucl and UCL Computer Science @uclcs
#AI #Agents #LLM #InformationRetrieval
Excited to share that the deadline for our AgentSearch Workshop @ SIGIR 2026 has been extended to
📅 𝗠𝗮𝘆 𝟴, 𝟮𝟬𝟮𝟲 (𝗔𝗼𝗘)
If you work on retrieval, AI agents, or tool use, we’d love to see your submissions.
🔗 https://t.co/VfT78O5C47
Excited to share that 3 of my papers are accepted to #ACL2026 🎉
I’m fortunate to contribute as a main author (1st/2nd author) across these works:
• 𝗕𝗲𝘆𝗼𝗻𝗱 𝗦𝘁𝗮𝘁𝗶𝗰 𝗧𝗼𝗼𝗹𝘀𝗲𝘁𝘀: 𝗦𝗲𝗹𝗳-𝗘𝘃𝗼𝗹𝘃𝗶𝗻𝗴 𝗟𝗟𝗠 𝗧𝗼𝗼𝗹 𝗔𝗴𝗲𝗻𝘁𝘀 𝘃𝗶𝗮 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗮𝗹 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻 (Findings)
Follow-up to my ACL work last year (paper: https://t.co/L3ldexgI7c), we study how LLM agents adapt to evolving tool ecosystems via continual documentation adaptation.
• 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗗𝗶𝗮𝗹𝗼𝗴𝘂𝗲 𝗥𝗲𝗳𝗶𝗻𝗲𝗺𝗲𝗻𝘁: 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗥𝗔𝗚 𝗼𝗻 𝗚𝗼𝗮𝗹-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗗𝗶𝗮𝗹𝗼𝗴𝘂𝗲𝘀 (Findings)
My internship work at Bloomberg — we adapt goal-oriented dialogues for retrieval-augmented QA.
• 𝗠𝗶𝘁𝗶𝗴𝗮𝘁𝗶𝗻𝗴 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗜𝗻𝘁𝗲𝗿𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗥𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗮𝗻𝗱 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗦𝗲𝗮𝗿𝗰𝗵 𝗔𝗴𝗲𝗻𝘁𝘀 (Main)
We investigate context interference in multi-turn search agents and propose a refinement-based solution.
Also sharing: 𝗣𝗥𝗘𝗙: 𝗥𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲-𝗙𝗿𝗲𝗲 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘀𝗲𝗱 𝗧𝗲𝘅𝘁 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗟𝗟𝗠𝘀 (not accepted this time)
A framework for evaluating personalised LLM outputs without requiring references
Grateful to my supervisors, collaborators, and teammates, especially my supervisor Emine @emine_yilm and Edgar @edgarmeij , my internship team lead @myahya , and my collaborators Boyang @BeyondHsueh 🙏
#ACL2026 #NLP #LLM #AIAgents #ToolUse #RAG #MachineLearning #AIResearch
@_sambhavkothari That’s an insightful observation. It makes me think CLI and code-MCP function more like planners, while token-MCP acts more like a task assigner. An orchestration layer could be the ideal way to bring out the best of both worlds.
📢 CFP: AgentSearch@SIGIR 2026
Submit your work on indexing, retrieval, and ranking of AI agents & tools.
📄 Extended abstracts (2p) & papers (4–9p)
📅 Deadline: Apr 15, 2026 (AoE)
🔗 https://t.co/aW69s0VAsC
#SIGIR#InformationRetrieval#AIagents