3 citations · 23 across the 66 of their papers we have counts for
39 papers · 1 filter
Balancing Memory Pathways: Analyzing and Improving Memory Utilization in Hybrid LMs
Hyunji Lee, Joykirat Singh, Zaid Khan +5
Recurrent-attention hybrid language models (LMs), which interleave attention and recurrent layers, are increasingly used to combine the efficiency of the recurrent layers with the…
GlossoGen: Emergent Language in Complex Multi-Agent LLM Interactions
Elias Stengel-Eskin, Newton Sander, Carlos Bonetti +4
The growing rate at which LLM agents interact with one another raises key questions about language evolution in multi-LLM-agent settings, with implications for safety and monitorab…
Who is the Agent to Blame? Localizing Faithfulness and Citation Mistakes in Agentic Deep Research
Eran Hirsch, David Wan, Han Wang +3
Deep research (DR) systems produce long-form cited reports by orchestrating multiple agents that search and synthesize information from the web. Citations are the primary mechanism…
CreativeInstruct: Scalably Teaching LLMs to Balance Quality, Creativity, and Diversity
Ananya Sahu, Mohit Bansal, Elias Stengel-Eskin
While post-training improves the capabilities of large language models (LLMs), it generally lowers their output diversity and creativity, negatively impacting tasks that explicitly…
CalVerT: Augmenting Agents with Calibrated Verifier Telemetry Improves Action and Learning in Knowledge-Intensive Tasks
Ashwin Vinod, Ying Ding, Elias Stengel-Eskin
LLM agents in knowledge intensive question answering take retrieval and reasoning actions with incomplete knowledge about whether their current answer is uncertain, unsupported, or…
PragReST: Self-Reinforcing Counterfactual Reasoning for Pragmatic Language Understanding
Jihyung Park, Minchao Huang, Leqi Liu +1
Natural language understanding often depends on meanings that are implied rather than explicitly stated, requiring pragmatic reasoning. Despite strong performance on math and logic…
MINTEval: Evaluating Memory under Multi-Target Interference in Long-Horizon Agent Systems
Hyunji Lee, Justin Chih-Yao Chen, Joykirat Singh +3
Real-world agents operate over long and evolving horizons, where information is repeatedly updated and may interfere across memories, requiring accurate recall and aggregated reaso…
Agent-BRACE: Decoupling Beliefs from Actions in Long-Horizon Tasks via Verbalized State Uncertainty
Joykirat Singh, Zaid Khan, Archiki Prasad +5
Large language models (LLMs) are increasingly deployed on long-horizon tasks in partially observable environments, where they must act while inferring and tracking a complex enviro…
MERRIN: A Benchmark for Multimodal Evidence Retrieval and Reasoning in Noisy Web Environments
Han Wang, David Wan, Hyunji Lee +6
Motivated by the underspecified, multi-hop nature of search queries and the multimodal, heterogeneous, and often conflicting nature of real-world web results, we introduce MERRIN (…
Playing Along: Learning a Double-Agent Defender for Belief Steering via Theory of Mind
Hanqi Xiao, Vaidehi Patil, Zaid Khan +3
As large language models (LLMs) become the engine behind conversational systems, their ability to reason about the intentions and states of their dialogue partners (i.e., form and…
Balancing Faithfulness and Performance in Reasoning via Multi-Listener Soft Execution
Nithin Sivakumaran, Shoubin Yu, Hyunji Lee +4
Chain-of-thought (CoT) reasoning sometimes fails to faithfully reflect the true computation of a large language model (LLM), hampering its utility in explaining how LLMs arrive at…
Multimodal Fact-Level Attribution for Verifiable Reasoning
David Wan, Han Wang, Ziyang Wang +3
Multimodal large language models (MLLMs) are increasingly used for real-world tasks involving multi-step reasoning and long-form generation, where reliability requires grounding mo…
Routing with Generated Data: Annotation-Free LLM Skill Estimation and Expert Selection
Tianyi Niu, Justin Chih-Yao Chen, Genta Indra Winata +6
Large Language Model (LLM) routers dynamically select optimal models for given inputs. Existing approaches typically assume access to ground-truth labeled data, which is often unav…
DART: Leveraging Multi-Agent Disagreement for Tool Recruitment in Multimodal Reasoning
Nithin Sivakumaran, Justin Chih-Yao Chen, David Wan +4
Specialized visual tools can augment large language models or vision language models with expert knowledge (e.g., grounding, spatial reasoning, medical knowledge, etc.), but knowin…
Gistify! Codebase-Level Understanding via Runtime Execution
Hyunji Lee, Minseon Kim, Chinmay Singh +10
As coding agents are increasingly deployed in large codebases, the need to automatically design challenging, codebase-level evaluation is central. We propose Gistify, a task where…
Generalized Correctness Models: Learning Calibrated and Model-Agnostic Correctness Predictors from Historical Patterns
Hanqi Xiao, Vaidehi Patil, Hyunji Lee +2
Generating accurate and calibrated confidence estimates is critical for deploying LLMs in high-stakes or user-facing applications, and remains an open challenge. Prior research has…
Language Models Identify Ambiguities and Exploit Loopholes
Jio Choi, Mohit Bansal, Elias Stengel-Eskin
Studying the responses of large language models (LLMs) to loopholes presents a two-fold opportunity. First, it affords us a lens through which to examine ambiguity and pragmatics i…
GrAInS: Gradient-based Attribution for Inference-Time Steering of LLMs and VLMs
Duy Nguyen, Archiki Prasad, Elias Stengel-Eskin +1
Inference-time steering methods offer a lightweight alternative to fine-tuning large language models (LLMs) and vision-language models (VLMs) by modifying internal activations at t…
GenerationPrograms: Fine-grained Attribution with Executable Programs
David Wan, Eran Hirsch, Elias Stengel-Eskin +2
Recent large language models (LLMs) achieve impressive performance in source-conditioned text generation but often fail to correctly provide fine-grained attributions for their out…
CLATTER: Comprehensive Entailment Reasoning for Hallucination Detection
Ron Eliav, Arie Cattan, Eran Hirsch +4
A common approach to hallucination detection casts it as a natural language inference (NLI) task, often using LLMs to classify whether the generated text is entailed by correspondi…
Instruction Tuning with and without Context: Behavioral Shifts and Downstream Impact
Hyunji Lee, Seunghyun Yoon, Yunjae Won +7
Instruction tuning is a widely used approach to improve the instruction-following ability of large language models (LLMs). Instruction-tuning datasets typically include a mixture o…
LAQuer: Localized Attribution Queries in Content-grounded Generation
Eran Hirsch, Aviv Slobodkin, David Wan +3
Grounded text generation models often produce content that deviates from their source material, requiring user verification to ensure accuracy. Existing attribution methods associa…
Retrieval-Augmented Generation with Conflicting Evidence
Han Wang, Archiki Prasad, Elias Stengel-Eskin +1
Large language model (LLM) agents are increasingly employing retrieval-augmented generation (RAG) to improve the factuality of their responses. However, in practice, these systems…
Executable Functional Abstractions: Inferring Generative Programs for Advanced Math Problems
Zaid Khan, Elias Stengel-Eskin, Archiki Prasad +2
Scientists often infer abstract procedures from specific instances of problems and use the abstractions to generate new, related instances. For example, programs encoding the forma…