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San Francisco
Conference
COLM 2026
October 6 - 8, 2026
San Francisco, California

Overview

COLM is an academic venue focused on the study of language modeling, broadly defined, with the goal of creating a community of researchers with expertise in different disciplines, focused on understanding, improving, and critiquing the development of LM technology.

Booth schedule

Presentations:

11:30 - 12:00 PM Talk: Super Weights in LLMs and the Failure of Selective Training - Akarsha Sehwag
12:00 - 12:30 PM Talk: BubbleScale: Predictive Environment Prewarming for Agentic RL Rollout - Sathishkumar Sivashanmugam
1:00 - 1:30 PM Talk: L3M: A Lean Harness for Multi-Agent Work - Carson Eisenach
1:30 - 2:00 PM Talk: Trie Automata for Constrained Decoding over Large Finite Sets - Xingzi Xu
2:00 - 2:30 PM Talk: Human-Centered Edge AI: From Robotics to AR Glasses - Yelin Kim

Meet the scientist:
11:00 AM - 12:00 PM

Domain/research

Team

Amazonian

Reinforcement learning, inference

eCF

Xingzi Xu

Generative AI, Inference-time compute, Multi-agent systems

AWS

Jack Butler

Generative AI, Large Language Models, Agentic AI

AWS Generative AI Innovation Center

Nikita Kozodoi

12:00 - 1:00 PM

Domain/research

Team

Amazonian

LLMs, Agents, Evaluation, Reasoning, Text Detection and Authorship Attribution

Worldwide Amazon Stores

Saranya Venkatraman

AI infra, KV Cache, vLLM etc

AWS

Sathishkumar Sivashanmugam

1:00 - 2:00 PM

Domain/research

Team

Amazonian

Multi-lingual LLMs, Post-Training, Alignment

Returns and ReCommerce

Shashwat Ranjan Chaurasia

Multimodal LLM, NeuroSymbolic and Multi-Agent Reasoning

AWS

Mofijul Islam

Human-Centered Edge AI, Robotics, AR Glasses

Amazon

Yelin Kim

4:30 - 5:30 PM

Domain/research

Team

Amazonian

Dingmin Wang

Haoyang Fang

CS Stores

Karlie Fang

Presentations:
11:30 - 12:00 PM Talk: OrchOpt: Validation-Gated Architecture Search for Self-Organizing Multi-Agent Orchestration - Amit Dhanda
12:00 - 12:30 PM Talk: Augmented Hypothesis Testing with Persona-Based LLM Simulations - Saab Mansour
1:00 - 1:30 PM Talk: Chart-RL: Policy Optimization Reinforcement Learning for Enhanced Visual Reasoning in Chart Question Answering with Vision Language Models - Amit Dhanda
2:00 - 2:30 PM Talk: Beyond Task Success: An Eight-Metric Tiered Evaluation Protocol for LLM Agents over Fragmented Operational Data - Neeraj Baji
4:30 - 5:00 PM Talk: Brick-DICL: Dynamic In-Context Learning for Automated Brick Schema Classification - Yiyue Qian

Meet the scientist:
11:00 AM - 12:00 PM

Domain/research

Team

Amazonian

Multimodal LLM, NeuroSymbolic and Multi-Agent Reasoning

AWS

Mofijul Islam

Dingmin Wang

Morteza Alizadeh

RL, LLMs, Multi-agent

SCOT

Carson Eisenach

12:00 - 1:00 PM

Domain/research

Team

Amazonian

CS Stores

Divyanshu Mishra

Reinforcement Learning, LLMs, Agentic Systems

AWS Marketplace

Amit Dhanda

1:00 - 2:00 PM

Domain/research

Team

Amazonian

Sina Ghotbi

Reasoning using LLMs, RL training LLMs and Agent Harnesses

WW Ops

Sreekar Aditya

Agent evaluation at scale, Personalization

WW Ops

Neeraj Baji

4:30 - 5:30 PM

Domain/research

Team

Amazonian

Multi-lingual LLMs, Post-Training, Alignment

Returns and ReCommerce

Shashwat Ranjan Chaurasia

Dynamic In-Context Learning, Brick Schema Classification

Yiyue Qian

Presentations:

11:30 - 12:00 PM Talk: AI rap battle demo: Can you out-rhyme the agent? - Nikita Kozodoi, Jack Butler
1:00 - 1:30 PM Talk: L3M: A Lean Harness for Multi-Agent Work - Carson Eisenach

Meet the scientist:
11:00 AM - 12:00 PM

Domain/research

Team

Amazonian

CS Stores

Karlie Fang

Multimodal LLM, NeuroSymbolic and Multi-Agent Reasoning

AWS

Mofijul Islam

DS Agent, Agent Evaluation, Data Synthesis, Agent Optimization

AWS

Chi Zhang

LLMs, Agents, Simulation, NLP, Multilinguality

AWS

Saab Mansour

12:00 - 1:00 PM

Domain/research

Team

Amazonian

Multi-lingual LLMs, Post-Training, Alignment

Returns and ReCommerce

Shashwat Ranjan Chaurasia

1:00 - 2:00 PM

Domain/research

Team

Amazonian

Afrin Dange

CS Stores

Divyanshu Mishra

4:30 - 5:30 PM

Domain/research

Team

Amazonian

NLP, Agentic AI

Stores

Arjun Mishra

Abhijeet Awasthi

Main conference publications

Workshops

COLM 2026 Workshop on Agent Behavior
October 9
Website: https://www.aiagentbehavior.com/
From trace entropy to coordination control: Diagnosing and intervening in multi-Agent LLM systems
Chinmoy Mohapatra, Suyash Vishnoi, Abhilasha Katariya, Rohit Malshe, Julian Pachon
2026
Machine learning
Beyond Task Success: An eight-metric tiered evaluation protocol for LLM agents over fragmented operational data
Neeraj Baji
2026
Machine learning

Work with us

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Applied Scientist, SCOT FO - SnT
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How does Amazon decide which fulfillment center ships your order, which truck carries it, and how to keep promises across hundreds of millions of packages daily? How does it decide how many trucks and how much labor are required to ship orders across the network? SCOT Fulfillment Optimization (FO) owns the optimization and forecasting science behind these decisions. We are seeking Applied Scientists to join the FO Science & Tech team in Barcelona (alternatively: Luxembourg or London) with a strong academic background in optimization, machine learning, and/or time-series forecasting. • You will design and build state-of-the-art machine learning and optimization models that power Amazon's fulfillment decisions at an unprecedented scale across two core scientific pillars: • Large-Scale Optimization and Planning: Designing planning systems for order assignment and resource utilization, while balancing multi-objective cost-speed tradeoffs to enable controllers to steer millions of shipments per hour optimally. • Demand Forecasting & Predictive ML: Developing time-series forecasts for customer demand, incorporating contextual information (weather, sales, order properties), and modeling uncertainty for core planning systems. Basic qualifications • PhD in Operations Research, Applied Mathematics, Computer Science, or related field (or equivalent experience) • Strong programming skills (Python preferred; experience with optimization solvers a plus) • Research experience in one or more: • Large-scale mathematical programming (LP, MIP, decomposition methods) • Combinatorial optimization (assignment, scheduling, network flows) • Multi-objective optimization and control • Large-scale time-series forecasting (GenAI models, probabilistic forecasting, uncertainty quantification) • Causal inference (spatiotemporal causal modeling, offline policy evaluation) Preferred qualifications • Experience building optimization systems that run in production at scale • Being comfortable with ambiguity and fast iteration cycles • Publications in relevant venues Key job responsibilities Design and implement optimization and forecasting models for large-scale fulfillment problems, from order assignment to network flow control. Build research prototypes end-to-end: from problem formulation through scalable implementation to production validation. Analyse complex tradeoffs (cost, speed, capacity, accuracy) and translate findings into actionable recommendations for leadership and operations teams. Collaborate with engineers to bring science solutions into production systems serving millions of customer orders daily. A day in the life You formulate an optimization or forecasting problem on a whiteboard with teammates, then prototype it in Python with real data by the afternoon. You run experiments against production-scale datasets, iterate on the model, and present results to stakeholders who will use them to make network decisions next week. Some days you dive deep into solver performance; other days you're explaining a Pareto frontier to an operations leader. You collaborate with large engineering and product teams to bring your solutions into systems serving millions of customers. Alongside fast-turnaround prototypes, you own long-term research bets, the kind that reshape how Amazon's fulfillment network operates at scale. Your work goes live. About the team SCOT Fulfillment Optimization Science & Tech (FO SnT) is the applied research team behind Amazon's fulfillment decision-making systems. We decide how orders get assigned to warehouses, how capacity is allocated across the network, and how cost and speed tradeoffs are managed in real time, at global scale. Our models influence billions of euros in annual operational spend. They protect sites from overload during peak, reduce transportation costs and CO2 emissions, and ensure customers receive their packages when promised. Leadership relies on our science to make investment decisions worth hundreds of millions. We are practitioners of large-scale optimization: MIP formulations, decomposition methods, approximation algorithms, and parallelisation. We use machine learning where it sharpens our decisions, including forecasting, learned heuristics, and multi-armed bandits. We pick the right tool for the problem, not the fashionable one. You will work alongside Senior and Principal scientists, and collaborate with Amazon Scholars and academic partners who bring frontier research into our applied problems. We code our prototypes to be production-ready and collaborate with large engineering teams to ship systems, not papers. Above all, we have fun solving hard real-world problems at real-world speed, failing, learning, and shipping along the way.
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