Meng Jiang

Frank M. Freimann Collegiate Professor at Notre Dame CSE | Data Mining | NLP | AI

[Meng Jiang @Meng_CS]

Big AI skills are distilled into small AI. Human skills are distilled into AI systems. But AI users worry about losing their own skills and ability to learn. Is distilling knowledge from AI into humans (through reading AI outputs, coding with AI, and other uses) simply too slow?

[Meng Jiang @Meng_CS]

My students keep telling me to tweet more, and I keep making excuses. One excuse is thinking every social media post has to be polished or joyful. I know that’s not true, so I’m practicing: one random thought a week.

[Meng Jiang @Meng_CS]

Best conference experience this year so far! Conference organizers are awesome; speakers are super super good!

[Braden Hancock @bradenjhancock]

@CAISconf was sold out but small, focused on a particular well-scoped area (compound agentic systems). Some workshop papers were submitted as late as just a couple weeks before (fresh content), and good thing, too! Agentic systems already look quite different today than even 3 months ago.

Perhaps the systems focus naturally attracts researchers who are inclined to build prototypes and libraries (not just publish), but I was still pleasantly surprised to see how many members of the Laude community had independently identified this conference as one they wanted to submit to and attend. On top of that, throwing a lounge after hours with food, drinks, demos, salon-style conversation, (and a mini podcast studio, because why not?) made it easy to get an even higher concentration of interesting people with interesting half-baked ideas in a particular niche that happens to be on fire right now.

Loved the experience. Well-done, CAIS organizers (@heathercmiller, @lateinteraction, @matei_zaharia, @deeptir18, et al). Same time, same place next year?

[Yuyang Bai @YuyangBai02]

🔥 New survey: Inference-Time Control for Trustworthy LLMs.

Once a model ships, training-time alignment can't keep up. Inference-time control is how we patch the gap.

🚀 We collected 200+ papers (2020–2026) and built the first pipeline-grounded taxonomy that unifies seven method families: context engineering, guardrails, decoding, representation engineering, unlearning, pruning, and multi-agent orchestration.

📄 Paper: https://t.co/mf8VZoBGzn

Existing surveys cut the field by harm type or by one method family. We treat inference-time interventions as a unified control plane over the generation pipeline.

💡 The Framework: Three tiers, by where the intervention happens.
🛡️ External: around the model (prompts, guardrails, decoding)
🧠 Internal: inside the model (steering, unlearning, pruning)
🤝 System-level: across models (debate, verification)

📊 The Coverage:
🔍 200+ papers indexed (2020 → 2026)
🧠 7 method categories, one pipeline-grounded map
📈 4 trustworthy dimensions ✕ 5 evaluation axes = a meta-axis grid

🗂️ Repo (live, 200+ papers indexed):
https://t.co/pGTiZ0K8hv
🌐 Website: https://t.co/YxhIhpidOl

👇 Full thread below.

[Souradip Chakraborty @SOURADIPCHAKR18]

🚨Typical RL algorithms and on-policy distillation methods are blind samplers: they use privileged info to score rollouts, but not to *find* them.

We ask: can we use privileged info to *actively sample* the rollouts RL wishes it can stumble upon with compute?

⤵️ Pedagogical RL

[John Kim @johnkimdw]

I’m thrilled to share that I’ll be starting my CS PhD at @NorthwesternU this fall, advised by @ManlingLi_! I’ll be researching areas in trustworthy AI and spatial intelligence to build reliable AI systems that are grounded in the physical world. I’m also happy to announce that I was awarded the @NSF GRFP fellowship, which will support my PhD for 3 years!

This wouldn’t have been possible without my wonderful mentors @nunompmoniz, @Meng_CS, @frank_liu_01, @NoahZiems, and countless others who’ve guided me throughout my undergrad.

And so… I guess I won’t be leaving the midwest :)

[Ming Li @ UMD PhD @Ming_Liiii]

Excited to share our ACL 2026 work, trying to solve the issue raised by the ICLR Outstanding Paper “LLMs Get Lost In Multi-Turn Conversation”!

Our RLAAR (https://t.co/CVUOavVtq7) is an RL framework that trains LLMs to both answer correctly and wait when context is insufficient, using verifiable accuracy and abstention rewards.

This tackles a key weakness in today’s conversational LLMs: they often answer too early, make wrong assumptions, and struggle to recover as conversations unfold.

We’re also excited to see this challenge highlighted by “LLMs Get Lost In Multi-Turn Conversation” (https://t.co/tISe06KGXW) being recognized as an ICLR 2026 Outstanding Paper.

Reliable conversational AI needs to know when to answer — and when to hold back.

#ACL2026 #ICLR2026 #LLM #RLVR #ConversationalAI

[Meng Jiang @Meng_CS]

@lateinteraction Some supercomputer has 10M CPU cores; we human bodies don't even have five cores. Too painful! (well, sometimes, I feel some people have 120 hours/day.)

[Lakshya A Agrawal @LakshyAAAgrawal]

Thrilled to present GEPA as an Oral Talk and Poster at ICLR 2026 this Friday in Rio! 🇧🇷

Apr 24
Oral Session 3A (Agents), 10:30 AM BRT, Amphitheater
Poster Session 4, 3:15 PM, Pavilion 3

https://t.co/aeFnyHNu6p

Let's recap what's happened since we released GEPA last year 🧵

[Meng Jiang @Meng_CS]

@matei_zaharia @databricks Congratulations!

[Meng Jiang @Meng_CS]

Decentralized RAG allows your database to benefit all LLM clients. On the other side, not all data sources are reliable. Managing source reliability on blockchain can avoid third-party manipulation. Introducing dRAG + Blockchain + Truth Discovery: https://t.co/JrMRZwQ6VI

[Peng Qi @qi2peng2]

𝗕𝗲𝗰𝗮𝘂𝘀𝗲 𝟵.𝟭𝟭>𝟵.𝟵 𝗮𝗻𝗱 𝗮 𝘁𝗿𝗶𝗮𝗻𝗴𝗹𝗲 𝗵𝗮𝘀 𝗳𝗼𝘂𝗿 𝘀𝗶𝗱𝗲𝘀, 𝘁𝗵𝗲𝗿𝗲𝗳𝗼𝗿𝗲 𝟭+𝟭=𝟮.

LLMs and Language Agents can sometimes generate correct answers from blatantly incorrect reasoning, which is more often in complex tasks, and exacerbated by reinforcement learning (RL), the commonly believed silver bullet to complex reasoning in LLMs.

This is due to a well-known phenomenon called reward hacking, where if the only training signal LLMs are getting from the training data exclusively regards the final result, then LLMs are incentivized to match the correct final output through whatever means possible on its training data, leading to inconsistent and ungeneralizable reasoning processes in RL's wake.

With our intern Mengzhao Jia, we (@ignaciocases and myself, plus folks from @Meng_CS
s lab at Notre Dame) explore a simple fix: can we use the LLM's own reasoning to provide some additional supervision signal for the reasoning process itself, so that besides the final result, the LLM is also encouraged to stay consistent in its reasoning during training?

We design an algorithm to automatically create rubrics for LLM reasoning processes, and train the model to adhere to these rubrics alongside generating correct final answers during RL. The resulting model not only produces significantly more consistent reasoning, but also generalizes better on a wide range of complex reasoning tasks we benchmarked, even with just 10% of the training data. We hope this technique helps pave the way to more powerful and generalizable reasoning models for complex tasks.

Read more in our preprint: https://t.co/8pG2cEVdIL

[Hy Dang @HyDang99]

Thrilled to share that “Improving Large Language Models Function Calling and Interpretability via Guided-Structured Templates” paper has been accepted to EMNLP 2025 (Main Conference)!🎉
📄 Check it out on
arXiv: https://t.co/oe3QwhJWQW
project page: https://t.co/W9l16gjxg2
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[Tarannum Zaki @tarannum_zaki]

.@DomSoos from @WebSciDL and @oducs is presenting "Can LLMs Beat Humans on Discerning Human-written and LLM-generated Science News?" They explored whether LLMs can outperform humans for LLM-generated vs. human written news.

🔗doi: 10.1145/3720553.3746674

#LLM #NLP @fanchyna

[Meng Jiang @Meng_CS]

@NoahZiems I've co-directed it for 6 months!!!
https://t.co/AhNgPTRX4a

[Meng Jiang @Meng_CS]

Job opportunity (postdoc at Notre Dame Foundation Models Lab):
https://t.co/pe1h3Ql9tx

[Yining Lu @Yining__Lu]

✴️ Pleased to introduce our new paper https://t.co/JyMPWvGDfP

- Rebalance multiobjectives during training through dynamic reward weighting
- Build Pareto-dominant front over static baselines across online RL algorithms, datasets, and model families
- Faster convergence rate

1/8

[Walter Scheirer @wjscheirer]

Come be my colleague! @ND_CSE at @NotreDame is hiring a tenure-track professor in computer vision! (And robotics and quantum.)

More info here: https://t.co/9xJwO2sfik

[Meng Jiang @Meng_CS]

@lateinteraction @NoahZiems @MIT_CSAIL @DSPyOSS Congratulations to both of you! Glad to see the "interaction"/collaboration is getting strong and soon fruitful - and never too "late" :) @lateinteraction I am so excited too!
Way to go, wonderful @NoahZiems ! Change the world with mind and hand!

[Gang Liu @gliu0329]

🔥 Only 15 days left! 🔥

The Open Polymer Challenge already has 9,800+ entrants and 38,000+ submissions. If you have not joined yet, let’s jump in these last few days to 🌍 accelerate polymer discovery with ML and go for 💰 $50,000 in prizes.

👉 LINK: https://t.co/FAQe20XTu6