Siddhant Bhambri — accepted AI-conference papers

2026

Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!

Subbarao Kambhampati, Karthik Valmeekam, Siddhant Bhambri, Vardhan Palod, Lucas Saldyt, Kaya Stechly +3

ICML 2026poster

Intermediate token generation (ITG), where a model produces output before the solution, has become a standard method to improve the performance of language models on reasoning tasks. These intermediate tokens have been called \say{reasoning traces} or even \say{thoughts} -- implicitly anthropomorphi…

Cited by 39 Source ↗Scholar ↗

2026

Who Is Helping Whom? Analyzing Inter-Dependencies to Evaluate Cooperation in Human-AI Teaming

Upasana Biswas, Vardhan Palod, Siddhant Bhambri, Subbarao Kambhampati

AAAI 2026technical

State-of-the-art methods for Human-AI Teaming and Zero-shot Cooperation focus on task completion i.e. task rewards, as the sole evaluation metric while being agnostic to `how

Cited by 0 Source ↗PDF ↗Scholar ↗

2024

Position: LLMs Can’t Plan, But Can Help Planning in LLM-Modulo Frameworks

Subbarao Kambhampati, Karthik Valmeekam, Lin Guan, Mudit Verma, Kaya Stechly, Siddhant Bhambri +2

ICML 2024spotlight

We argue that auto-regressive LLMs cannot, by themselves, do planning or self-verification (which is after all a form of reasoning), and shed some light on the reasons for misunderstandings in the literature. We will also argue that LLMs should be viewed as universal approximate knowledge sources th…

Cited by 192 Source ↗PDF ↗Scholar ↗

2021

Contrastively Learning Visual Attention as Affordance Cues from Demonstrations for Robotic Grasping

Yantian Zha, Siddhant Bhambri, Lin Guan

IROS 2021poster

Conventional works that learn grasping affordance from demonstrations need to explicitly predict grasping configurations, such as gripper approaching angles or grasping preshapes. Classic motion planners could then sample trajectories by using such predicted configurations. In this work, our goal is…

Cited by 9 Source ↗Scholar ↗