{
 "schema_version": 1,
 "date": "2026-10-01",
 "timezone": "Asia/Seoul",
 "rotation": {
  "day_index": 273,
  "position": 273,
  "cycle_length_days": 1167,
  "epoch": "2026-01-01",
  "method": "stable-hash permutation walked by days-since-epoch; reproducible & non-repeating"
 },
 "paper": {
  "uid": "iclr-2026-oral-111",
  "conference": "ICLR",
  "year": 2026,
  "track": "Oral",
  "title": "Structured Flow Autoencoders: Learning Structured Probabilistic Representations with Flow Matching",
  "authors": [
   "Yidan Xu",
   "Yixin Wang",
   "XuanLong Nguyen"
  ],
  "affiliation": "",
  "summary": "A framework that composes any probabilistic graphical model with flow matching, jointly learning structured latent representations and high-fidelity generative models through a single objective.",
  "links": {
   "openreview": "https://openreview.net/forum?id=KYdfvF2SZN",
   "arxiv": "",
   "detail": ""
  }
 },
 "candidates": [
  {
   "uid": "neurips-2025-oral-56",
   "conference": "NeurIPS",
   "year": 2025,
   "track": "Oral",
   "title": "On Linear Mode Connectivity of Mixture-of-Experts Architectures",
   "authors": [
    "Viet-Hoang Tran",
    "Van-Hoan Trinh",
    "Khanh Vinh Bui",
    "Tan Minh Nguyen"
   ],
   "affiliation": "National University of Singapore",
   "summary": "We investigate Linear Mode Connectivity (LMC) in Mixture-of-Experts (MoE) architectures by analyzing their underlying permutation symmetries and proposing expert-matching algorithms that align independently trained MoEs to reveal LMC.",
   "links": {
    "openreview": "https://openreview.net/forum?id=RF3miSqdXa",
    "arxiv": "",
    "detail": ""
   }
  },
  {
   "uid": "neurips-2025-spotlight-287",
   "conference": "NeurIPS",
   "year": 2025,
   "track": "Spotlight",
   "title": "SANSA: Unleashing the Hidden Semantics in SAM2 for Few-Shot Segmentation",
   "authors": [
    "Claudia Cuttano",
    "Gabriele Trivigno",
    "Giuseppe Averta",
    "Carlo Masone"
   ],
   "affiliation": "",
   "summary": "Few-shot segmentation aims to segment unseen categories from just a handful of annotated examples",
   "links": {
    "openreview": "https://openreview.net/forum?id=o8r3gOFTQo",
    "arxiv": "",
    "detail": ""
   }
  },
  {
   "uid": "neurips-2025-spotlight-488",
   "conference": "NeurIPS",
   "year": 2025,
   "track": "Spotlight",
   "title": "Projection-based Lyapunov method for fully heterogeneous weakly-coupled MDPs",
   "authors": [
    "XiangCheng Zhang",
    "Yige Hong",
    "Weina Wang"
   ],
   "affiliation": "",
   "summary": "Heterogeneity poses a fundamental challenge for many real-world large-scale decision-making problems but remains largely understudied",
   "links": {
    "openreview": "https://openreview.net/forum?id=alw3e1Qa7I",
    "arxiv": "",
    "detail": ""
   }
  },
  {
   "uid": "iclr-2026-oral-13",
   "conference": "ICLR",
   "year": 2026,
   "track": "Oral",
   "title": "AstaBench: Rigorous Benchmarking of AI Agents with a Scientific Research Suite",
   "authors": [
    "Jonathan Bragg",
    "Mike D'Arcy",
    "Nishant Balepur",
    "Dan Bareket",
    "Bhavana Dalvi Mishra",
    "Sergey Feldman",
    "Dany Haddad",
    "Jena D. Hwang",
    "Peter Jansen",
    "Varsha Kishore",
    "Bodhisattwa Prasad Majumder",
    "Aakanksha Naik",
    "Sigal Rahamimov",
    "Kyle Richardson",
    "Amanpreet Singh",
    "Harshit Surana",
    "Aryeh Tiktinsky",
    "Rosni Vasu",
    "Guy Wiener",
    "Chloe Anastasiades",
    "Stefanus Candra",
    "Jason Dunkelberger",
    "Daniel Emery",
    "Rob Evans",
    "Malachi Hamada",
    "Regan Huff",
    "Rodney Kinney",
    "Matt Latzke",
    "Jaron Lochner",
    "Ruben Lozano-Aguilera",
    "Ngoc-Uyen Nguyen",
    "Smita Rao",
    "Amber Tanaka",
    "Brooke Vlahos",
    "Peter Clark",
    "Doug Downey",
    "Yoav Goldberg",
    "Ashish Sabharwal",
    "Daniel S Weld"
   ],
   "affiliation": "Allen Institute for Artificial Intelligence",
   "summary": "We present principles and tooling for rigorous AI agent benchmarking, instantiated in AstaBench—the first holistic measure of agentic ability for scientific research—plus experiments showing AI remains far from solving research assistance.",
   "links": {
    "openreview": "https://openreview.net/forum?id=M7TNf5J26u",
    "arxiv": "",
    "detail": ""
   }
  }
 ]
}