Davit Abrahamyan

PAN Team, Jiannan Xiang, Yi Gu et al.

A world model enables an intelligent agent to imagine, predict, and reason about how the world evolves in response to its actions, and accordingly to plan and strategize. While recent video generation models produce realistic visual sequences, they typically operate in the prompt-to-full-video manner without causal control, interactivity, or long-horizon consistency required for purposeful reasoning. Existing world modeling efforts, on the other hand, often focus on restricted domains (e.g., physical, game, or 3D-scene dynamics) with limited depth and controllability, and struggle to generalize across diverse environments and interaction formats. In this work, we introduce PAN, a general, interactable, and long-horizon world model that predicts future world states through high-quality video simulation conditioned on history and natural language actions. PAN employs the Generative Latent Prediction (GLP) architecture that combines an autoregressive latent dynamics backbone based on a large language model (LLM), which grounds simulation in extensive text-based knowledge and enables conditioning on language-specified actions, with a video diffusion decoder that reconstructs perceptually detailed and temporally coherent visual observations, to achieve a unification between latent space reasoning (imagination) and realizable world dynamics (reality). Trained on large-scale video-action pairs spanning diverse domains, PAN supports open-domain, action-conditioned simulation with coherent, long-term dynamics. Extensive experiments show that PAN achieves strong performance in action-conditioned world simulation, long-horizon forecasting, and simulative reasoning compared to other video generators and world models, taking a step towards general world models that enable predictive simulation of future world states for reasoning and acting.

Pratham Yashwante, Davit Abrahamyan, Shresth Grover et al.

We study how visual artifacts introduced by diffusion-based inpainting affect language generation in vision-language models. We use a two-stage diagnostic setup in which masked image regions are reconstructed and then provided to captioning models, enabling controlled comparisons between captions generated from original and reconstructed inputs. Across multiple datasets, we analyze the relationship between reconstruction fidelity and downstream caption quality. We observe consistent associations between pixel-level and perceptual reconstruction metrics and both lexical and semantic captioning performance. Additional analysis of intermediate visual representations and attention patterns shows that inpainting artifacts lead to systematic, layer-dependent changes in model behavior. Together, these results provide a practical diagnostic framework for examining how visual reconstruction quality influences language generation in multimodal systems.

Jie JW Wu, Manav Chaudhary, Davit Abrahamyan et al. · stanford

Large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks. However, a gap remains between their output and the problem-solving strategies of human developers. Unlike humans, who spend substantial time disambiguating requirements through iterative dialogue, LLMs often generate code despite ambiguities in natural language requirements, leading to unreliable solutions. Different from prior work, we study whether a Code LLM can be fine-tuned to learn clarification-seeking behavior. While recent work has focused on LLM-based agents for iterative code generation, we argue that the ability to recognize and query ambiguous requirements should be intrinsic to the models themselves, especially in agentic AI where models and humans collaborate. We present ClarifyCoder, a framework with synthetic data generation and instruction-tuning that fine-tunes an LLM to identify ambiguities and request clarification before code generation. Our approach has two components: (1) a data synthesis technique that augments programming datasets with scenarios requiring clarification to generate clarification-aware training data, and (2) a fine-tuning strategy that teaches models to prioritize seeking clarification over immediate code generation when faced with incomplete or ambiguous requirements. We also provide an empirical analysis of integrating ClarifyCoder with standard fine-tuning for joint optimization of clarification-awareness and coding ability. Experimental results show that ClarifyCoder achieves a 63% communication rate (40% absolute increase) and a 52% good question rate (30% absolute increase) on ambiguous tasks, significantly improving LLMs' communication capabilities while maintaining code generation performance.

Davit Abrahamyan, Fatemeh H. Fard

Developers spend much time finding information that is relevant to their questions. Stack Overflow has been the leading resource, and with the advent of Large Language Models (LLMs), generative models such as ChatGPT are used frequently. However, there is a catch in using each one separately. Searching for answers is time-consuming and tedious, as shown by the many tools developed by researchers to address this issue. On the other, using LLMs is not reliable, as they might produce irrelevant or unreliable answers (i.e., hallucination). In this work, we present StackRAG, a retrieval-augmented Multiagent generation tool based on LLMs that combines the two worlds: aggregating the knowledge from SO to enhance the reliability of the generated answers. Initial evaluations show that the generated answers are correct, accurate, relevant, and useful.