2 citations · 2 across the 9 of their papers we have counts for
13 papers
Test-Time Adaptation of Reasoning Strategies with Bayesian Nonparametric Memory
Keshav Ramji, Tahira Naseem, Ramón Fernandez Astudillo
While modern large language models (LLMs) have been trained to reason through verbalized chains-of-thought, the generation cost grows substantially due to suboptimal paths to reach…
The Surprising Effectiveness of Shared Memory in Looped Transformers
Giovanni Monea, Keshav Ramji, Yousef El-Kurdi +4
Looped Transformers apply the same layers several times per token, adding compute to improve quality without more parameters. Each recursion, however, writes its own key-value cach…
Learning to Predict Distributions over Weight Updates for Test-Time Adaptation
Azal Ahmad Khan, Keshav Ramji, Tahira Naseem +2
Hypernetworks have recently shown success in dynamically adapting the parameters of Large Language Models (LLMs) at runtime based on signals such as task descriptions or additional…
Thinking Without Words: Efficient Latent Reasoning with Abstract Chain-of-Thought
Keshav Ramji, Tahira Naseem, Ramón Fernandez Astudillo
While long, explicit chains-of-thought (CoT) have proven effective on complex reasoning tasks, they are costly to generate during inference. Non-verbal reasoning methods have emerg…
DRBENCHER: Can Your Agent Identify the Entity, Retrieve Its Properties and Do the Math?
Young-Suk Lee, Ramon Fernandez Astudillo, Radu Florian
Deep research agents increasingly interleave web browsing with multi-step computation, yet existing benchmarks evaluate these capabilities in isolation, creating a blind spot in as…
Do LLMs Benefit From Their Own Words?
Jenny Y. Huang, Leshem Choshen, Wei Sun +5
In multi-turn conversations, large language models typically condition on the full conversation history: both past user prompts and assistant responses. We revisit this design choi…
Insertion Based Sequence Generation with Learnable Order Dynamics
Dhruvesh Patel, Benjamin Rozonoyer, Gaurav Pandey +3
Existing insertion-based masked diffusion models that generate sequences by interleaving token insertion with unmasking use fixed schedules that are not dependent on the data. For…
Latency and Token-Aware Test-Time Compute
Jenny Y. Huang, Mehul Damani, Yousef El-Kurdi +2
Inference-time scaling has emerged as a powerful way to improve large language model (LLM) performance by generating multiple candidate responses and selecting among them. However,…
Optimal Policy Minimum Bayesian Risk
Ramón Fernandez Astudillo, Md Arafat Sultan, Aashka Trivedi +4
Inference scaling helps LLMs solve complex reasoning problems through extended runtime computation. On top of long chain-of-thought (long-CoT) models, purely inference-time techniq…
Latent Principle Discovery for Language Model Self-Improvement
Keshav Ramji, Tahira Naseem, Ramón Fernandez Astudillo
When language model (LM) users aim to improve the quality of its generations, it is crucial to specify concrete behavioral attributes that the model should strive to reflect. Howev…
Multi-Document Grounded Multi-Turn Synthetic Dialog Generation
Young-Suk Lee, Chulaka Gunasekara, Danish Contractor +2
We introduce a technique for multi-document grounded multi-turn synthetic dialog generation that incorporates three main ideas. First, we control the overall dialog flow using taxo…
The Future of Open Human Feedback
Shachar Don-Yehiya, Ben Burtenshaw, Ramon Fernandez Astudillo +17
Human feedback on conversations with language language models (LLMs) is central to how these systems learn about the world, improve their capabilities, and are steered toward desir…
Self-Refinement of Language Models from External Proxy Metrics Feedback
Keshav Ramji, Young-Suk Lee, Ramón Fernandez Astudillo +5
It is often desirable for Large Language Models (LLMs) to capture multiple objectives when providing a response. In document-grounded response generation, for example, agent respon…