
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-…
Matt C. retweeted
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!💙
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
@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
#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.
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
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!
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.
This might be most unique goodie bag item ever at a conference: Hungarian paprika 🌶️ #EMNLP2026 @emnlpmeeting – at Budapest, Hungary
Matt C. retweeted
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 ↓
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!
Matt C. retweeted
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
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.
Matt C. retweeted
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
Matt C. retweeted
The Kimi K3 architecture figure for yesterday's big open-weight model release, along with some observations and thoughts.
1. Yes, it looks relatively complicated, but it's essentially a scaled-up production version of their Kimi Linear model they released last year (scaled up from 48B -> 2.8T; K3 is by far the biggest open-weight model right now)
2. The one new component compared to Kimi Linear is the LatentMoE. I omitted it in the figure below since it's already very crowded, but that's essentially the same LatentMoE as in Nemotron 3 Ultra (you can find it in my LLM Architecture Gallery if you are curious). The idea here is to compress (down-project) large linear layers similar to multi-head latent attention.
3. Kimi K3's overall trend (similar to Nemotron 3, DeepSeek V4, and others) is also towards better inference efficiency. That is, there are many components that replace existing components with efficiency-tweaked versions. I.e., MoE -> LatentMoE, regular attention -> multi-head latent attention and Kimi Delta Attention. (I also have short tutorials and write-ups in my gallery if you are curious about additional details).
4. The one component change that is not an efficiency tweak is attention residuals. Like DeepSeek V4 improved the residual path with mHC (manifold-constrained Hyper-Connections), attention residuals are a way to improve the residual path, but it works a bit differently. I.e., mHC made the residual path wider. Attention residuals (also already part of Kimi Linear) connect the residuals across layers; the connection itself uses an attention score for an important/contribution weight. According to the report, it improves the validation loss and downstream performance (a bit) consistently and adds about 4% in training cost and 2% in inference cost.
5. Interestingly, Kimi K3 got rid of all RoPE layers and uses NoPE (No Positional Embeddings) everywhere instead. (Again, this is inherited from Kimi Linear). In other architectures, the recent trend was towards RoPE in local attention layers (like sliding window attention) and NoPE in the global layers. There were a few architectures that only used NoPE everywhere, but this is the first frontier-level one as far as I know.
6. Kimi K3 now also has native multimodal support, which is great!
There are several other interesting training tidbits in the technical report, but that's it from the architecture front so far. A really great release overall.
Matt C. retweeted
Our models are way over parameterized, our learning algorithms are still incredibly slow, our machines are too power hungry, and our understanding of intelligence is too shallow. The path goes much, much further. It terrifies me how early we are
Theory is cheap; now onto real, practical problems. After using @OpenAI GPT 5.6 Sol for a while, it seems increasingly likely that LMs will reduce the need for empiricists to collaborate with theorists and increase the workload of verifying and demonstrating practical utility. In many ways, this is the time for those who understand theory and application, who can't beat theorists on theory but can't beat domain experts on application, to shine. Novel theories that used to take months/years to prove and inspire novel methods and applications can now be generated in a couple of hours -- set up 5.6 Sol with xhigh or ultra thinking and let it loose. The only thing that's left is to put together practical/real-life examples that verify the theory and beat baseline methods. The latter is much more difficult to do, but I'd imagine in a few months, this will change once LMs learn better taste. All of this really revolutionizes the research landscape.
I solved 6 open Erdős problems in 5 days, using @OpenAI GPT-5.6 Sol.
I have a math background, but the Codex workflow I used does not require deep mathematical knowledge.
Here’s exactly how I approached it, including my prompts 🧵
Matt C. retweeted
Was there anything in the Medical Imaging space that people thought was really cool at #icml2026? Self-promotion welcome! Especially funky or creative ideas


























