Min-Yen Kan

📄 Paper: sentic.net/ai-overload.pdf ✍️Feedback and discussion from the community is welcome. Huge thanks to Philipp for the inspiring talk! @Philipp_Mayr 🧵5/5

🔍 Key insight: In AI systems, the bottleneck is no longer generation but oversight. As AI autonomy grows, validation, monitoring, and accountability burdens increase. Mitigation requires bounded autonomy, calibrated reliance, governance, and AI literacy. 🧵4/5

We were delighted to host Philipp Mayr (GESIS – Leibniz Institute for the Social Sciences) at our group meeting! 🎉 He presented the Knowledge Technologies department, the Information & Data Retrieval team, the OMINO project, and two Scholarly Document Processing projects. 🧵1/5

Bottom line: Better fact-checking isn’t about more web access. It’s about finding what’s missing. Search the gaps → better Notes. If you’re working on misinformation, moderation, or Community Notes, we’d love your thoughts. (6/6)

Human evaluation (N=100): 🏆 69% win rate vs human-written helpful notes 📈 Helpfulness: 3.87 vs 3.36 📌 Biggest gain: better context Also, 59% win vs generic web agents. Search strategy matters. (5/6)

To test it, we built PolBench: 📊 78,698 U.S. political tweets 📝 169,992 Community Notes ⚖️ 92% stuck in “Needs More Ratings” Spans pre/post LLM cutoffs for REAL-WORLD testing. Open-source gold for moderation AI devs! Who's experimenting? Tag us! #Dataset #Politics (3/6)

The idea is simple: Don’t search everything. Search for what’s missing. GitSearch: 1️⃣ Finds the info gaps 2️⃣ Prioritizes the big ones 3️⃣ Pulls targeted evidence 4️⃣ Writes grounded notes Search with a plan > search at random. (2/6)

🚨 Can smarter search beat generic web-search LLMs for Community Notes? Same model. Same web access. Different search strategy. We built GitSearch to test this. 📄 buff.ly/pZij5eZ (1/6)

🚨 Building agents? How do agents learn whether agents are trustworthy? @skyriver_2000's preprint tells us how to build interaction history to guide agents in juding peers' reliability! Take a 👀! 👉 arxiv.org/abs/2601.21742 (code:🐙github.com/skyriver-2000/…) w/ @soujanyaporia

📌 Final thought Trust is a learnable, usable signal for LLM agents. Towards reliable multi-agent systems, we must teach agents who to believe — not just how to reason. 🔗 arXiv: arxiv.org/abs/2601.21742 🏠 Github: github.com/skyriver-2000/… 💬 Thoughts welcome! 🧵6/n

⚠️ Why this matters Without trust modeling: ❌ agents collapse under social pressure ❌ confident hallucinations dominate ❌ adversarial peers win With ECL: ✅ agents resist blind conformity ✅ trust becomes an explicit reasoning signal Analytic experiments verify this 🧵5/n

🔥 Thrilled to announce ECL, a framework for LLMs to reason with trust in multi-agent systems 📖 Key Takeaways •We introduce interaction history for LLMs to judge peer reliability and selectively refer to them •We decouple trust estimation and conditioned decision-making 🧵1/n

Multimodal? Symbolic? and Reasoning? Ans: MuSLR! arxiv.org/abs/2509.25851 Come to the #AAAI2026 Bridge programme in 48hrs ⏰. Lead by Jundong Xu (w/ @ScottNLP Yuhui Zhang @PanLiangming Qijun Huang @preslav_nakov @knmnyn @WilliamWangNLP Mong-Li Lee & Wynne Hsu) @NUSComputing

We are #hiring for Postdoctoral Positions with WING.NUS at National University of Singapore. Message me if you're interested in joining our team. We are attending The 40th Annual AAAI Conference on Artificial Intelligence if you would like to meet! - via #Whova event app