Publications

VLOD-TTA: Test-Time Adaptation of Vision-Language Object Detectors
Atif Belal
Heitor Rapela Medeiros
Marco Pedersoli
Eric Granger
2025-09-30
ArXiv (prépublication)
doi.org
arxiv.org
A brain-inspired agentic architecture to improve planning with LLMs
Taylor Webb
Shanka Subhra Mondal
Ida Momennejad
Large language models (LLMs) demonstrate impressive performance on a wide variety of tasks, but they often struggle with tasks that require … (voir plus)multi-step reasoning or goal-directed planning. To address this, we take inspiration from the human brain, in which planning is accomplished via component processes that are predominantly associated with specific brain regions. These processes include conflict monitoring, state prediction, state evaluation, task decomposition, and task coordination. We find that LLMs are often capable of carrying out these functions in isolation, but struggle to autonomously coordinate them in the service of a goal. Therefore, we propose a modular agentic architecture - the Modular Agentic Planner (MAP) - in which planning is performed via the interaction of specialized brain-inspired LLM modules. We evaluate MAP on three challenging planning tasks – graph traversal, Tower of Hanoi, and the PlanBench benchmark – as well as an NLP task requiring multi-step reasoning (strategyQA). We find that MAP yields significant improvements over both standard LLM methods and competitive agentic baselines, can be effectively combined with smaller and more cost-efficient LLMs, and displays superior transfer across tasks. These results demonstrate the benefit of utilizing knowledge from cognitive neuroscience to improve planning in LLMs. Multi-step planning is a challenge for LLMs. Here, the authors introduce a brain-inspired Modular Agentic Planner that decomposes planning into specialized LLM modules, improving performance across tasks and highlighting the value of cognitive neuroscience for LLM design.
2025-09-29
Nature Communications (publié)
doi.org
DeepCodeProbe: Evaluating Code Representation Quality in Models Trained on Code
Vahid Majdinasab
Amin Nikanjam
Foutse Khomh
2025-09-29
Empirical Software Engineering (publié)
doi.org
DRBench: A Realistic Benchmark for Enterprise Deep Research
Amirhossein Abaskohi
Tianyi Chen
Miguel Muñoz-Mármol
Curtis Fox
Amrutha Varshini Ramesh
Étienne Marcotte
Xing Han Lu
Nicolas Chapados
Spandana Gella
Christopher Pal
Alexandre Drouin
Issam Hadj Laradji
We introduce DRBench, a benchmark for evaluating AI agents on complex, open-ended deep research tasks in enterprise settings. Unlike prior b… (voir plus)enchmarks that focus on simple questions or web-only queries, DRBench evaluates agents on multi-step queries (for example, ``What changes should we make to our product roadmap to ensure compliance with this standard?") that require identifying supporting facts from both the public web and private company knowledge base. Each task is grounded in realistic user personas and enterprise context, spanning a heterogeneous search space that includes productivity software, cloud file systems, emails, chat conversations, and the open web. Tasks are generated through a carefully designed synthesis pipeline with human-in-the-loop verification, and agents are evaluated on their ability to recall relevant insights, maintain factual accuracy, and produce coherent, well-structured reports. We release 15 deep research tasks across 10 domains, such as Sales, Cybersecurity, and Compliance. We demonstrate the effectiveness of DRBench by evaluating diverse DR agents across open- and closed-source models (such as GPT, Llama, and Qwen) and DR strategies, highlighting their strengths, weaknesses, and the critical path for advancing enterprise deep research. Code is available at https://github.com/ServiceNow/drbench.
2025-09-29
ArXiv (prépublication)
doi.org
arxiv.org
Generative Floor Plan Design with LLMs via Reinforcement Learning with Verifiable Rewards
Luis Lara
Zhi Hao Luo
Aristides Milios
Aditya Sharma
Ge Ya Luo
Christopher Beckham
Florian Golemo
Christopher Pal
An AI system for professional floor plan design needs to be able to precisely control room dimensions and areas (quantitative constraints), … (voir plus)while also balancing functional considerations and design aesthetics. Existing generative approaches focus primarily on respecting the requested connectivity between rooms, but do not support generating floor plans with numerical constraints. We introduce a text‑based floor plan generation approach that fine-tunes a large language model (LLM) on real plans and then applies reinforcement learning with verifiable rewards (RLVR) to enforce both numerical (areas, dimensions) and spatial (topological) constraints. Furthermore, we design a set of constraint adherence metrics to measure how generated floor plans align with user-defined constraints systematically. Our model generates floor plans that satisfy numerical constraints and outperforms existing methods on realism, compatibility, and diversity scores. Specifically, our approach leads to an up to 94\% reduction in compatibility score. Our results demonstrate that LLMs can effectively handle quantitative constraints in structured design tasks, suggesting broader applications for text-based generative modeling.
2025-09-29
NeurIPS.cc/2025/Workshop/UrbanAI (présentation orale)
openreview.net
openreview.net
GRPO-λ: Credit Assignment improves LLM Reasoning
Prasanna Parthasarathi
Mathieu Reymond
Boxing Chen
Yufei Cui
A. Chandar
Large language models (LLMs) are increasingly deployed for tasks requiring complex reasoning, prompting significant interest in improving th… (voir plus)eir reasoning abilities through post-training. Especially RL based methods using verifiable reward, like the state-of-the-art GRPO, have shown to tremendously improve reasoning behaviors when applied as post-training methods. However, the lack of an explicit reward or critic model limits GRPO's ability to assign fine-grained credit across token sequences. In this work, we present GRPO-
2025-09-29
ArXiv (prépublication)
doi.org
arxiv.org
HVAC-SPICE: Value-Uncertainty In-Context RL with Thompson Sampling for Zero-Shot HVAC Control
Anaïs Berkes
Vincent Taboga
Donna Vakalis
David Rolnick
Yoshua Bengio
Urban buildings consume 40\% of global energy, yet most rely on inefficient rule-based HVAC systems due to the impracticality of deploying a… (voir plus)dvanced controllers across diverse building stock. In-context reinforcement learning (ICRL) offers promise for rapid deployment without per-building training, but standard supervised learning objectives that maximise likelihood of training actions inherit behaviour-policy bias and provide weak exploration under the distribution shifts common when transferring across buildings and climates. We present SPICE (Sampling Policies In-Context with Ensemble uncertainty), a novel ICRL method specifically designed for zero-shot building control that addresses these fundamental limitations. SPICE introduces two key methodological innovations: (i) a propensity-corrected, return-aware training objective that prioritises high-advantage, high-uncertainty actions to enable improvement beyond suboptimal training demonstrations, and (ii) lightweight value ensembles with randomised priors that provide explicit uncertainty estimates for principled episode-level Thompson sampling. At deployment, SPICE samples one value head per episode and acts greedily, resulting in temporally coherent exploration without test-time gradients or building-specific models. We establish a comprehensive experimental protocol using the HOT dataset to evaluate SPICE across diverse building types and climate zones, focusing on the energy efficiency, occupant comfort, and zero-shot transfer capabilities that are critical for urban-scale deployment.
2025-09-29
NeurIPS.cc/2025/Workshop/UrbanAI (poster)
openreview.net
openreview.net
Large Pre-Trained Models for Bimanual Manipulation in 3D
Hanna Yurchyk
Wei-Di Chang
Gregory Dudek
David Meger
2025-09-29
IEEE-RAS Conference on Humanoid Robots (publié)
doi.org
arxiv.org
MalGPT: A Generative Explainable Model for Malware Binaries
Mohd Saqib
Benjamin C. M. Fung
Steven H. H. Ding
Philippe Charland
2025-09-29
Lecture Notes in Computer Science (publié)
doi.org
Recursive Self-Aggregation Unlocks Deep Thinking in Large Language Models
Siddarth Venkatraman
Vineet Jain
Sarthak Mittal
Vedant Shah
Johan Samir Obando Ceron
Yoshua Bengio
Brian R. Bartoldson
Bhavya Kailkhura
Guillaume Lajoie
Glen Berseth
Nikolay Malkin
Moksh J. Jain
Test-time scaling methods improve the capabilities of large language models (LLMs) by increasing the amount of compute used during inference… (voir plus) to make a prediction. Inference-time compute can be scaled in parallel by choosing among multiple independent solutions or sequentially through self-refinement. We propose Recursive Self-Aggregation (RSA), a test-time scaling method inspired by evolutionary methods that combines the benefits of both parallel and sequential scaling. Each step of RSA refines a population of candidate reasoning chains through aggregation of subsets to yield a population of improved solutions, which are then used as the candidate pool for the next iteration. RSA exploits the rich information embedded in the reasoning chains -- not just the final answers -- and enables bootstrapping from partially correct intermediate steps within different chains of thought. Empirically, RSA delivers substantial performance gains with increasing compute budgets across diverse tasks, model families and sizes. Notably, RSA enables Qwen3-4B-Instruct-2507 to achieve competitive performance with larger reasoning models, including DeepSeek-R1 and o3-mini (high), while outperforming purely parallel and sequential scaling strategies across AIME-25, HMMT-25, Reasoning Gym, LiveCodeBench-v6, and SuperGPQA. We further demonstrate that training the model to combine solutions via a novel aggregation-aware reinforcement learning approach yields significant performance gains. Code available at https://github.com/HyperPotatoNeo/RSA.
2025-09-29
ArXiv (prépublication)
doi.org
arxiv.org
Asymmetric developmental bifurcations in polarized environments: a new class of human variants, which may include autism.
Laurent Mottron
Alix Lavigne-Champagne
Boris Bernhardt
Guillaume Dumas
Sébastien Jacquemont
David Gagnon
2025-09-28
Molecular Psychiatry (publié)
doi.org
BloomAPR: A Bloom's Taxonomy-based Framework for Assessing the Capabilities of LLM-Powered APR Solutions
Yinghang Ma
Jiho Shin
Leuson Da Silva
Zhen Ming (Jack) Jiang
Song Wang
Foutse Khomh
Shin Hwei Tan
Recent advances in large language models (LLMs) have accelerated the development of AI-driven automated program repair (APR) solutions. Howe… (voir plus)ver, these solutions are typically evaluated using static benchmarks such as Defects4J and SWE-bench, which suffer from two key limitations: (1) the risk of data contamination, potentially inflating evaluation results due to overlap with LLM training data, and (2) limited ability to assess the APR capabilities in dynamic and diverse contexts. In this paper, we introduced BloomAPR, a novel dynamic evaluation framework grounded in Bloom's Taxonomy. Our framework offers a structured approach to assess the cognitive capabilities of LLM-powered APR solutions across progressively complex reasoning levels. Using Defects4J as a case study, we evaluated two state-of-the-art LLM-powered APR solutions, ChatRepair and CigaR, under three different LLMs: GPT-3.5-Turbo, Llama-3.1, and StarCoder-2. Our findings show that while these solutions exhibit basic reasoning skills and effectively memorize bug-fixing patterns (fixing up to 81.57% of bugs at the Remember layer), their performance increases with synthetically generated bugs (up to 60.66% increase at the Understand layer). However, they perform worse on minor syntactic changes (fixing up to 43.32% at the Apply layer), and they struggle to repair similar bugs when injected into real-world projects (solving only 13.46% to 41.34% bugs at the Analyze layer). These results underscore the urgent need for evolving benchmarks and provide a foundation for more trustworthy evaluation of LLM-powered software engineering solutions.
2025-09-28
ArXiv (prépublication)
doi.org
arxiv.org