Research
The problems I want to solve, where the work lands, and the papers.
They lean on memorized patterns instead of transferable, compositional structure. How do we train for genuine generalization?
Learning happens in slow, offline training runs, and every action waits on the whole model. We need to model the cerebellum: a fast forward model that predicts the next step, carries the routine ones itself, and skips the brain loop entirely, waking the slow model only when its prediction misses. That is what a real-time agent needs, most of all a computer-use agent, where every click waits on a round trip through the big model.
Experience arrives once, in order, and never comes back. Learning from it needs a stronger state store than a context window, and learning without backprop that consolidates what matters into memory as the stream passes, rather than waiting for the next offline training run.
A single large model that holds all the power is a single point of failure, and high-level autonomy cannot rest on one model policing itself. Safety should come from a society shared by humans and agents: many specialized agents, each with its own safeguards, and a balance of power strong enough that when a share of the agents break, the rest can repair the society, as human societies do. The powers are separated: one body writes the laws, another interprets them and judges disputes, and a third enforces them, with hierarchical reporting so that every action answers to someone, and levels of access and permission, including tools whose whole purpose is to constrain other agents. The levers of self-change stay out of reach: reward systems and training pathways are controlled and closed to the agents, and copying weights is banned. Each agent’s freedom and resources are allocated by the society’s consensus rules and laws.
Every token is chosen by scoring it against a vocabulary of hundreds of thousands, and that classifier head is the bottleneck for both generation latency and the long tail, where rare tokens are starved of probability. A human does not compare hundreds of thousands of words before producing each one: the activation drives the actuator directly. Generation should work the same way: a truly generative process that emits the output, not a discriminative one that ranks every candidate at every step.
papers
8- safetypost-training
Do Not Be Angry at Your Agent: Insights from Human-AI Collaborative Problem Solving
When an agent gets it wrong over a long multi-turn trajectory, should the human restart it, steer it neutrally, or steer it emotionally? Across twelve models and five benchmarks the best strategy depends on the agent (restarting wins for instruction-tuned models, emotional steering pays for reasoning-specialised ones), but one constraint survives: anger is never the right choice. A Jacobian-lens readout shows why, angry feedback pulls the deep-layer workspace toward apology and agreement and away from task content, turning a correction into sycophantic capitulation.
- safetysynthetic data
Should We Type or Talk to LLM Agents? A Comprehensive Study of Voice and Keyboard Input Perturbations
Typing and speaking leave different signatures on a prompt: orthographic noise from keyboards, disfluency and restructuring from transcription. HIVE perturbs both channels to measure the cost. Voice perturbations lower accuracy on every instruction-tuned model tested and it is the structure of the transcription, not its fillers, that carries the cost; keyboard noise costs far less. Both trace to one cause: how many of the question’s tokens survive. A thinking budget recovers the keyboard channel almost entirely but leaves speech untouched.
- memorypost-training
SHRED: Retain-Set-Free Unlearning via Self-Distillation with Logit Demotion
Most LLM unlearning methods need a curated retain set to stop general utility collapsing. SHRED needs only the forget set: the highest-information tokens in each instance become forget positions, the rest stay as benign anchors, and one top-k KL self-distillation objective demotes memorized logits while preserving the distribution elsewhere. Pareto-optimal forget/utility trade-off on four benchmarks, robust to relearning and membership-inference attacks.
- safetypost-training
Expert Personas Improve LLM Alignment but Damage Accuracy: Bootstrapping Intent-Based Persona Routing with PRISM
Persona effectiveness is task-type dependent: expert prompts consistently improve alignment-dependent tasks (safety, preference) but reliably damage pretraining-dependent knowledge retrieval. PRISM teaches models when to invoke a persona via intent-based self-modeling, preserving accuracy while keeping alignment gains.
- latencymemory
AttendTwice: Long-Context Inference via Dynamic Chunk-Level KV-Cache Selection
Two attention paths. A cheap low-rank path scores which chunks of the KV cache matter for a query; the exact path then attends only over the chunks it selected, so most of the cache is never loaded. Selecting contiguous chunks rather than scattered tokens is what makes the gather fast enough to pay for itself; per-token selection is exact, but its scattered access pattern costs more than it saves.
- synthetic datasafety
Multimodal Synthetic Data Finetuning and Model Collapse
Studies how vision-language models degrade when fine-tuned on AI-generated multimodal data. Characterizes the collapse dynamics specific to the multimodal regime and proposes mitigation strategies that preserve diversity across modalities.
- pretraininglatency
Lateralization MLP: A Simple Brain-inspired Architecture for Diffusion
A brain-inspired MLP architecture with hemispheric lateralization applied to diffusion models. Shows competitive sample quality at reduced parameter count, suggesting structured asymmetry as an inductive bias for generative modeling.
- pretraininglatency
Static Key Attention in Vision
A more efficient attention variant for vision transformers that pre-computes a static key projection, reducing per-token compute while maintaining downstream task performance.
academic service
reviewing
- NeurIPS 2024–2026
- ICLR 2024–2025
- ICML 2024–2025
teaching assistant
- DSCI 552 Machine Learning for Data Science (2023, 2025)
- CSCI 576 Multimedia Systems Design (2022, 2024, 2025)
- CSCI 567 Machine Learning (2022, 2024)
- CSCI 566 Deep Learning and Its Applications (2024)
- CSCI 544 Applied Natural Language Processing (2023)