Matt C. (@matte_ce) profile picture

ML/CV PhD @RiceUniversity. Currently learning about UQ, LMs and Agents. I’m also on the job market!

Bay Area CA / Houston TX
Joined June 2020
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Cool! Won an outstanding paper award #EMNLP2026 @emnlpmeeting . Not bad for a vision/imaging/UQ guy. Conformal Certification of Reasoning Trace Prefixes: aclanthology.org/2026.emnlp-…
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i've reproduced the results from the MIT blog post today on weight-space programming. but one additional result i have to add: this is a new, step-change emerging capability that's appeared in just the latest set of models.
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We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math. It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training. We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵 Blog: rikiyat.github.io/blog/weigh…
Readers added context they thought people might want to know
LLM was given weights explicitly as text in prompt and used code/forward passes (per authors' logs) to ablate Qwen looping direction, a known thinking-mode issue per model card. Primarily fixed repetition in that mode, not general 'natively' improving weights/math. rikiyat.github.io/blog/weight-sp… github.com/RikiyaT/lm_und… huggingface.co/Qwen/Qwen3.5-0… huggingface.co/RikiyaT/Qwen3.…
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1/ ML is shifting towards “foundation models are all you need.” These huge networks can do many things, but even fine-tuning now demands unreasonable amounts of GPU memory. How can parameter-efficient fine-tuning (PEFT) help? A thread 🪢
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Matt C. retweeted
I wrote about my experience on the industry job market as a PhD student in an AI-adjacent field (HCI): kjfeng.me/blog/industry-job-…. If you’re wrapping up your PhD, didn’t focus on LLM training, and thinking about industry jobs, this one’s for you!
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Excited to share that we received two Outstanding Paper Awards at #EMNLP2026🔥 Congratulations to the student leads and the teams!🎉 Many thanks to the program chairs and committee for this recognition!💙
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I asked my Muse agent to count how many papers were awarded at #EMNLP2026. There are 34(!) awarded papers compared to 12 last year. My guess is this accounts for the almost 2x increase in submissions and acceptances, and excludes the SAC highlights (35 last year). Maybe best paper per track? I guess we’ll find out tomorrow! How it was counted (from Muse): filtered the Underline program to oral sessions only (70 sessions), opened each one, and counted the trophy icon above a paper entry. Here's the full list: - In-Place Feedback: Reliable Refinement for Multi-Turn Expert-LLM Collaboration - Conformal Certification of Reasoning Trace Prefixes - Counterfactual Constraint-Conditioned On-Policy Distillation for Multi-Constraint Instruction Following - Format as Nuisance: Intervention-Inspired Cross-Format Distillation for Knowledge Conflicts - Do Benchmarks Measure the Same Thing? Evaluation-Free Benchmark Redundancy Analysis with SAE - UReason: Benchmarking Reasoning-to-Generation Alignment in Unified Multimodal Models - InterPol: De-anonymizing LM Arena via Interpolated Preference Learning - Race in the Record: Measuring Racial Signal and Model Inference in Police Arrest Narratives - The Cost of Sounding Different: Accent Bias in Audio Language Models - SWE-MEM: Learning Adaptive Memory Management for Long-Horizon Coding Agents - Gold vs. Translation: Are We Measuring Language, Knowledge, or Locality in Translated Benchmarks? - Rethinking Clustering as a Conversational Supervision Problem - Who Counts as "the People"? Measuring Contested Political Concepts from Political Speech using Masked Language Models - Accelerating Constrained Decoding with Token Space Compression - OptiMer: Optimal Distribution Vector Merging Is Better than Data Mixing for Continual Pre-Training - Clinically Grounded Privacy Evaluation of Medical LMs - Can MLLMs Perceive and Describe Medical Image Quality Beyond Scores? - When Must Faithful Chain-of-Thought Fail? An Information-Theoretic Bound and Its Empirical Limits - From Debate to Decision: Conformal Social Choice for Safe Multi-Agent Deliberation - Language Identification for Non-Standard Varieties: Language Varieties as Geolects on a Geodesic Grid - Aligned but Not Partner-Specific: How Multimodal LLM Agents Succeed in Reference Games Without Forming Conceptual Pacts - SkillHarm: Lifecycle-Aware Skill-Based Attacks via Automated Construction - Omnilingual MT: Machine Translation for 1,600 Languages - Cognitively Decoupled Meta-Induction for Out-of-Distribution Generalization - Text as a Universal Interface for Transferable Personalization - From Profiles to Steering Vectors: Global Sparse Priors and Local Semantic Calibration for Personalized Text Generation - When Hate Meets Facts: LLMs-in-the-Loop for Check-worthiness Detection in Hate Speech - Tampered ML: Evaluating LLM Judgment of Machine Learning Research - Diffusion Large Language Models for Visual Speech Recognition - Repetition over Diversity: High-Signal Data Filtering for Sample-Efficient German Language Modeling - HarnessForge: Joint Harness and Policy Evolution for Adaptive Agent Systems - Beyond "To whom it may concern": Tailoring Machine Translation to Audience and Intent - Rethinking the Evaluation of Efficiency Methods for Multi-Agent Systems - Why and When to Use Statistical Significance in Explanation Evaluation: A Systematic Survey and Guidelines for Explainable NLP
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@shocheen correctly points out that posters were also awarded. I asked my Muse agent to follow up on this and it found an additional 14 more awarded papers, including the one mentioned by @virajtiran: - AlphaToken: Decoupling Adaptation and Stability for Path-Aware Response Token Valuation in LLM Post-Training - Don't Read Everything: A Curvature-Conditioned Query for Linear Attention - Self-Supervised On-Policy Distillation for Omni-Modal Video Captioning - How Reliable are AMR Parser Outputs? Uncertainty Estimation for AMR Parsing - From Token Probabilities to Semantic Constraints: Towards Declarative Probabilistic Evaluation of Language Models - Beyond Next-Observation Prediction: Agent-Authored World Modeling for Sequential Decision Making - Whether LLMs Can Navigate Beliefs and Facts Depends on How You Phrase It - The Dialect Tax: Dialectal Biases Persist throughout the Language Modeling Pipeline - VISA: Value Injection via Shielded Adaptation for Personalized LLM Alignment - Modular Representation Steering for Multi-Attribute LLM Alignment - D3-Gym: Constructing Real-World Verifiable Environments for Data-Driven Discovery - Double Trouble: Bilingual Pretraining Leaves Language-Conditioned Effects in Shared-Language Representations - XYBench: Can LLMs Respond Pragmatically to Queries with Misconceptions? - DiscoTrace: Representing and Comparing Answering Strategies of Humans and LLMs in Information-Seeking Question Answering
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#EMNLP2026 Not going to name names, but there are oral presenters who clearly struggle with English and have friends ask them questions to fill in the Q&A time. Answers are clearly rehearsed.
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Great under-cited paper talked about by Lillian Lee (Cornell) at #EMNLP2026: “RExBench: Can coding agents autonomously implement AI research extensions?” Currently, the answer is a resounding no. Stronger models achieved higher overall execution success rates, but they tended to introduce more implicit errors and over-editing due to making broader or more complex code changes. arxiv.org/abs/2506.22598
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Presenting at #EMNLP2026 Interp and Analysis 1!
Happy to share that our paper "Conformal Certification of Reasoning Trace Prefixes" arxiv.org/abs/2605.30085 was accepted at EMNLP 2026 (Main)! We introduce prefix length as a new practical uncertainty object for conformal calibration -- how much of a sequential trace can be safely retained. We find that this procedure can improve downstream repair, particularly when using repair models from a different family than the original. Looking forward to learning more about LMs in Budapest!
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My main takeaway: AI reviewing still sucks and REALLY hates when you don’t show statistical significance
This afternoon at #EMNLP2026: the preliminary report on the AI Reviewing Experiment at 14:00, followed by the panel on New Missions in NLP at 14:30. Orals/Posters B begin at 16:00.
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Matt C. retweeted
EMNLP📈
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This might be most unique goodie bag item ever at a conference: Hungarian paprika 🌶️ #EMNLP2026 @emnlpmeeting – at Budapest, Hungary
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X-rays are everywhere in medicine, but extracting general, quantitative anatomical information from them remains remarkably difficult. Announcing FleXray: an open-source, open-weight model for zero-shot anatomical segmentation across the human body—from different regions, viewing angles, and acquisition settings. FleXray learns from large-scale synthetic supervision generated from 3D anatomy, then transfers directly to real clinical X-rays. Try it now on your own X-rays in our in-browser demo! 🧵 1/N — project page, demo, weights, code & paper below ↓
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Happy to share that our paper "Conformal Certification of Reasoning Trace Prefixes" arxiv.org/abs/2605.30085 was accepted at EMNLP 2026 (Main)! We introduce prefix length as a new practical uncertainty object for conformal calibration -- how much of a sequential trace can be safely retained. We find that this procedure can improve downstream repair, particularly when using repair models from a different family than the original. Looking forward to learning more about LMs in Budapest!
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This is quite an interesting paper... DEXA scans are used to measure bone density and body composition. Here, authors trained a LeJEPA model on 11,540 DEXA scans which can then be used to extract clinically meaningful, whole-body representations of body. For example, the model can predict Type 2 Diabetes, osteoporosis, biological age, and so much more. Link to paper: arxiv.org/abs/2608.02208
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Matt C. retweeted
Replying to @_chenglou
I was part of that team. Basically ChatGPT one year before it came out. Called LMChat and then another codename. Google was too nervous to release it and DeepMind was blocked from shipping products that could disrupt Google. I think about this a lot.
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Students often ask me: why do so many choose 5% as the threshold for significance (“alpha”)? So let me try to explain the main rationale 1/20
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