We sat down with Rao Kambhampati, a Professor of CS at Arizona State University and former President of AAAI, to talk about reasoning models: what they are, when they work, and when they break.

Rao has been working on planning and decision-making since long before deep learning, which makes him one of the most grounded voices on what today's reasoning systems actually do. We start with definitions of what reasoning is, why planning is the hard subset of it, and what changed when systems like o1 and DeepSeek R1 moved the verifier from inference into post-training. From there we get into where these models generalize, where they don't, and why benchmarks can be misleading about both.

A big chunk of the conversation is on chain-of-thought: what intermediate tokens are actually doing, why they help the model more than they help the reader, and what outcome-based RL does to whatever semantic content was there to begin with. We also cover world models and why Rao thinks the video-only framing is the wrong bet, the difference between agentic safety and existential risk, and what the planning community figured out decades ago that the LLM community keeps rediscovering.

• (00:12) Intros
• (01:32) Defining "reasoning" and the System 1 / System 2 framing
• (04:12) Blocksworld vs Sokoban, and non-ergodicity
• (06:42) Pre-o1: PlanBench and "LLMs are zero-shot X" papers
• (07:42) LLM-Modulo and moving the verifier into post-training
• (10:12) Is RL post-training reasoning, or case-based retrieval?
• (13:12) τ-Bench and benchmarks that avoid action interactions
• (14:12) OOD generalization and what we don't know about post-training data
• (19:02) Does it matter how they work if they answer the questions we care about?
• (21:27) Architecture lotteries and why no one tries different designs
• (23:42) Intermediate tokens and the "reduce thinking effort" cottage industry
• (26:12) The 30×30 maze experiment
• (27:42) Sokoban, NetHack, and Mystery Blocksworld
• (34:58) Stop Anthropomorphizing Intermediate Tokens — the swapped-trace experiment
• (46:12) Latent reasoning, Coconut, and why R0 beat R1
• (50:12) How outcome-based RL erodes CoT semantics
• (52:12) Dot-dot-dot and Anthropic's CoT monitoring paper
• (53:42) Safety: Hinton, Bengio, LeCun
• (57:12) Existential risk vs real safety work
• (59:42) World models, transition models, and video-only approaches
• (1:03:12) Why linguistic abstractions matter — pick and roll
• (1:05:42) What the planning community knew in 2005
• (1:08:12) Multi-agent LLMs
• (1:09:57) Closing thoughts: the bridge analogy

Music:

• "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• "Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• Changes: trimmed

About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

In this episode, we hosted Zhuang Liu, Assistant Professor at Princeton and former researcher at Meta, for a conversation about what actually matters in modern AI and what turns out to be a historical accident.

Zhuang is behind some of the most important papers in recent years (with more than 100k citations): ConvNeXt (showing ConvNets can match Transformers if you get the details right), Transformers Without Normalization (replacing LayerNorm with dynamic tanh), ImageBind, Eyes Wide Shut on CLIP's blind spots, the dataset bias work showing that even our biggest "diverse" datasets are still distinguishable from each other, and more.

We got into whether architecture research is even worth doing anymore, what "good data" actually means, why vision is the natural bridge across modalities but language drove the adoption wave, whether we need per-lab RL environments or better continual learning, whether LLMs have world models (and for which tasks you'd need one), why LLM outputs carry fingerprints that survive paraphrasing, and where coding agents like Claude Code fit into research workflows today and where they still fall short.

Timeline

00:13 — Intro

01:15 — ConvNeXt and whether architecture still matters

06:35 — What actually drove the jump from GPT-1 to GPT-3

08:24 — Setting the bar for architecture papers today

11:14 — Dataset bias: why "diverse" datasets still aren't

22:52 — What good data actually looks like

26:49 — ImageBind and vision as the bridge across modalities

29:09 — Why language drove the adoption wave, not vision

32:24 — Eyes Wide Shut: CLIP's blind spots

34:57 — RL environments, continual learning, and memory as the real bottleneck

43:06 — Are inductive biases just historical accidents?

44:30 — Do LLMs have world models?

48:15 — Which tasks actually need a vision world model

50:14 — Idiosyncrasy in LLMs: pre-training vs post-training fingerprints

53:39 — The future of pre-training, mid-training, and post-training

57:57 — Claude Code, Codex, and coding agents in research

59:11 — Do we still need students in the age of autonomous research?

1:04:19 — Transformers Without Normalization and the four pillars that survived

1:06:53 — MetaMorph: Does generation help understanding, or the other way around?

1:09:17 — Wrap

Music:

• "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• "Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• Changes: trimmed

About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

We talked with Sasha Rush, researcher at Cursor and professor at Cornell, about what it actually feels like to we in the heart of the AI revolution and build coding agents right now. Sasha shared how these systems are changing day-to-day work and how it feels to develop these systems.

A big part of the conversation was about why coding has become such a powerful setting for these tools. We discussed what makes code different from other domains, why agents seem to work especially well there, and how much of today’s progress comes not just from better models, but from better ways of using them. Sasha also gave an inside look at how Cursor thinks about training coding models, long-running agents, context limits, bug finding, and the balance between autonomy and human oversight.

We also talked about the broader shift happening in software engineering. Are developers moving to a higher level of abstraction? Is this just a phase where we “babysit” models, or the beginning of a deeper change in how software gets built? Sasha had a very thoughtful perspective here, including what he’s seeing from students, researchers, and engineers who are growing up native to these tools.

More broadly, this episode is about what it means to do serious technical work in a moment when the tools are changing incredibly fast. Sasha brought both optimism and skepticism to the discussion, and that made this a really grounded conversation about where coding agents are today, what they are already surprisingly good at, and where all of this might be going next.

Timeline
00:00 Intro and Sasha joins us
01:11 What “coding agents” actually mean
02:34 Why coding became the breakout use case
08:56 Long-running agents and autonomous workflows
15:08 How these tools are changing the work of engineers
17:15 Are people just babysitting models right now?
22:11 How Cursor builds its coding models
26:29 Rewards, training, and what makes agents work
34:53 Memory, continual learning, and agent communication
38:00 How context compaction works in practice
41:29 Why coding agents recently got much better
50:31 Refactoring, maintenance, and self-improving codebases
52:16 Bug finding, oversight, and verification
54:43 Will this pace of progress continue?
56:42 Can this spread beyond coding?
58:27 The future of Cursor and coding agents
1:03:08 Model architectures beyond standard transformers
1:05:37 World models, diffusion, and what may come next

Music:

• "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• "Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• Changes: trimmed

About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.