š„Top AI Papers of the Week
The Top AI Papers of the Week (June 2 - 8)
1. The Illusion of Thinking
Investigates the capabilities and limitations of frontier Large Reasoning Models (LRMs) like Claude 3.7, DeepSeek-R1, and OpenAIās o-series by systematically analyzing their performance on reasoning tasks as a function of problem complexity. Rather than relying on conventional math benchmarks, which suffer from contamination and lack structure, the authors evaluate LRMs using four controllable puzzles (Tower of Hanoi, Checker Jumping, River Crossing, and Blocks World) that allow fine-grained complexity scaling and transparent trace analysis.
Key findings:
Three complexity regimes: The study identifies distinct performance phases. In low-complexity tasks, non-thinking LLMs outperform LRMs due to more efficient and direct computation. In medium complexity, reasoning models show an advantage, leveraging longer chain-of-thoughts to correct errors. However, in high complexity, all models, regardless of their reasoning scaffolds, collapse to near-zero accuracy.
Counterintuitive reasoning collapse: Surprisingly, LRMs reduce their reasoning effort (i.e., number of tokens used in thoughts) as problem complexity increases beyond a threshold. This suggests an internal scaling failure not caused by token limits but by intrinsic model behavior.
Reasoning trace inefficiencies: LRMs frequently āoverthinkā on simple problems, finding correct answers early but continuing to explore incorrect paths. For moderate tasks, they correct late, and for complex ones, they fail to find any valid solution. Position-based accuracy analysis of thoughts reveals systematic shifts in when correct solutions are generated within the trace.
Failure to execute explicit algorithms: Even when supplied with correct pseudocode (e.g., Tower of Hanoi recursion), models still failed at similar complexity points. This indicates that LRMs donāt just struggle to find solutions; they canāt reliably execute logical instructions either.
Inconsistent behavior across puzzles: Models could perform >100 correct steps in Tower of Hanoi (N=10) but fail after 4 steps in River Crossing (N=3), suggesting performance correlates more with training data familiarity than inherent problem complexity.
2. From Tokens to Thoughts
This paper introduces an information-theoretic framework to examine whether LLMs organize semantic knowledge like humans, balancing compression and meaning. Drawing from Rate-Distortion Theory and the Information Bottleneck principle, the authors evaluate token embeddings from 30+ LLMs against classic human categorization benchmarks from cognitive psychology.
LLMs do form broad conceptual categories that align well with human groupings. Adjusted Mutual Information scores show LLM clusters consistently outperform random baselines, with even small encoder models like BERT matching or beating larger decoder-only models on this alignment task.
However, LLMs struggle with fine-grained semantics. When tested on their ability to mirror human notions of item typicality (e.g., robin as a more typical bird than penguin), correlations between LLM embedding similarity and human ratings were weak and inconsistent. Most models failed to capture graded prototype structures evident in human cognition.
Using their unified loss function L (balancing information complexity and semantic distortion), the authors find that LLMs produce statistically efficient clusters with lower entropy and distortion, while human conceptual clusters are less compact but preserve richer nuance. This suggests LLMs over-optimize for compression at the expense of meaning, unlike humans, who tolerate inefficiency to retain adaptive, flexible structure.
The paper concludes that while LLMs can mimic surface-level categorization, they diverge fundamentally in how they represent meaning, highlighting a core gap between artificial and human semantic systems and offering a quantitative tool for improving human-aligned conceptual representations.
3. Knowledge or Reasoning
Introduces a fine-grained evaluation framework to dissect LLM thinking into two components: knowledge correctness and reasoning informativeness, measured via Knowledge Index (KI) and Information Gain (InfoGain), respectively. The authors apply this framework to evaluate how reasoning transfers across domains, particularly medical and mathematical, using Qwen2.5-7B and its DeepSeek-R1-distilled variant trained via SFT and RL.
Key findings include:
SFT improves knowledge but can harm reasoning: Supervised fine-tuning improves factual accuracy (e.g., 6.2% KI gain in medical tasks), but often leads to verbose or redundant reasoning that reduces InfoGain by 38.9% on average, compared to the base model.
RL boosts both reasoning and knowledge in medical settings: Reinforcement learning enhances reasoning clarity and prunes incorrect knowledge, leading to a 12.4-point average gain in KI. It improves inference by guiding models toward more factually sound reasoning paths.
Domain matters: While math tasks benefit more from reasoning (higher InfoGain), medical tasks rely heavily on domain knowledge (higher KI). In fact, KI shows a stronger correlation (0.998) with task accuracy than InfoGain (0.698) in medical benchmarks.
Base models outperform R1-distilled versions in medicine: Qwen-Base consistently outperforms DeepSeek-R1-distilled models across accuracy, InfoGain, and KI. The R1-distilled model struggles with medical adaptation, likely due to pretraining bias toward math/code domains
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4. Open Thoughts
This paper presents OpenThoughts3, a systematic recipe for curating supervised fine-tuning (SFT) data that advances the performance of open-source reasoning models. The authors develop OpenThinker3-7B, a 7B parameter model trained on their new 1.2M example dataset (OpenThoughts3-1.2M) derived from over 1,000 controlled experiments. Despite using no reinforcement learning, OpenThinker3-7B outperforms all other open-data 7B and 8B models on standard math, code, and science reasoning benchmarks, even beating models trained with larger-scale or mixed SFT+RL pipelines.
Key insights and contributions:
Best-in-class 7B open model: OpenThinker3-7B achieves state-of-the-art results on AIME25 (53.3%), LiveCodeBench (51.7%), and GPQA Diamond (53.7%), outperforming DeepSeek-R1-Distill-Qwen-7B by 15ā20 percentage points across tasks.
Scaling laws with clean design: The authors ablate every step in the data pipeline, question sourcing, filtering, teacher choice, deduplication, and answer sampling, showing how each incrementally lifts performance. For instance, using multiple answers per question (16Ć) improved results more than simply increasing question diversity.
QwQ-32B as a better teacher than stronger models: Surprisingly, QwQ-32B yielded better student models than DeepSeek-R1 or Phi-4 despite lower benchmark scores, suggesting teacher choice affects trace quality more than raw performance.
Filtering matters more than verification: Question filtering based on response length and LLM-estimated difficulty was more predictive of downstream gains than traditional heuristics (e.g., fastText) or even filtering based on correctness verification, which had negligible effects.
Data quality over diversity: Mixing only the top 1ā2 question sources per domain consistently outperformed using many sources, indicating that question quality is more important than dataset heterogeneity.
Open-source impact: The full datasets and models are released at openthoughts.ai, providing a reproducible benchmark for open reasoning research.
5. Coding Agents with Multimodal Browsing
Introduces OpenHands-Versa, a unified agent designed to perform strongly across diverse domains, coding, web browsing, and multimodal information access, by equipping a single agent with three general capabilities: code execution, multimodal web browsing, and file/search access. In contrast to specialist or multi-agent systems optimized for narrow domains, OpenHands-Versa aims to solve a wide variety of real-world tasks with minimal architectural complexity.
Key highlights:
Unified Toolset, Superior Coverage: OpenHands-Versa integrates visual web browsing, search API access, and multimodal file processing into the OpenHands coding framework. Despite its simplicity, it surpasses specialized agents across three benchmarks: SWE-Bench Multimodal (+9.1%), GAIA (+1.3%), and The Agent Company (+9.1%) in success rates.
Benchmark Generalization: The agent matches or outperforms multi-agent systems like OWL-roleplaying and Magentic-One, which struggle to generalize across domains. For example, OWL-roleplaying, though strong on GAIA, performs poorly on The Agent Company due to limited tool generality.
Domain-Aware Tool Use: Analysis reveals that OpenHands-Versa effectively adapts its tool usage per benchmark (e.g., search APIs in GAIA, browser in The Agent Company, and visual validation in SWE-Bench M), unlike its predecessor, OpenHands, which misuses or lacks crucial tools like search.
Minimal Agent, Strong Results: By relying on a single-agent design and Claude-3.7 or Claude Sonnet-4 as backbone LLMs, OpenHands-Versa achieves SOTA results without per-task tool customization. For example, it attains 64.24% on GAIA val split, outperforming multi-agent baselines by up to +18%.
6. Self-Challenging Language Model Agents
Proposes a novel self-improvement method for multi-turn tool-use LLM agents, called the Self-Challenging Agent (SCA). It trains LLMs entirely from tasks they generate themselves, avoiding the need for human-annotated tasks or evaluations. The framework introduces a new task format called Code-as-Task (CaT), ensuring generated tasks are feasible, verifiable, and challenging. SCA is shown to double performance in a self-improvement setting and significantly boost performance in distillation.
Key contributions and findings:
Self-generated tasks via dual-agent roles: The agent alternates between a challenger role, where it explores the environment and creates tasks, and an executor role, where it learns to solve these tasks via reinforcement learning. The process is designed to emulate how human annotators interact with tools to design meaningful tasks.
Code-as-Task (CaT) formulation: Each synthetic task includes an instruction, a Python-based verification function, a working solution, and several failure cases. This structure ensures task quality by filtering out trivial, impossible, or non-verifiable tasks using automatic code execution checks.
Strong results in both distillation and self-improvement: SCA improves the Llama-3.1-8B-Instruct modelās success rate from 12.0% to 23.5% when learning from its own tasks. In the distillation setting (using a 70B teacher), SCA lifts performance to 32.2% Pass@1, outperforming the prior PAE baseline across all tool-use environments.
Human annotation and ablation confirm task quality: Tasks generated with CaT significantly reduce false positives and negatives compared to PAE. A detailed analysis shows CaTās filtering removes flawed tasks while retaining diversity when used with stronger models like Llama-3.1-70B.
Scaling and training dynamics: More diverse tasks (not just more trajectories per task) yield better generalization, emphasizing the importance of broad synthetic coverage. Online RL methods like PPO and GRPO can further boost performance, but at higher tuning and compute cost.
7. AlphaOne
Introduces a universal framework, Ī±1, for modulating the reasoning progress of large reasoning models (LRMs) during inference. Rather than relying on rigid or automatic schedules, Ī±1 explicitly controls when and how models engage in āslow thinkingā using a tunable parameter Ī±. The method dynamically inserts āwaitā tokens to encourage deeper reasoning and then deterministically ends slow thinking with a ā</think>ā token to prompt efficient answer generation. This yields better accuracy and efficiency than previous test-time scaling approaches.
Key insights:
Slow-then-fast reasoning outperforms other strategies: Contrary to human intuition (fast-then-slow), models benefit from beginning with slow reasoning before transitioning to faster inference. This āfrontloaded effortā schedule leads to more accurate problem solving.
Dense modulation via Ī±1 boosts accuracy and efficiency: By continuously adjusting reasoning pace via Ī±-scheduled āwaitā token insertions, Ī±1 outperforms existing test-time strategies like s1 (monotonic increase) and CoD (monotonic decrease), achieving up to +6.15% accuracy gain while using up to 14% fewer tokens on some benchmarks.
Linear annealing is the most effective scheduling strategy: Among several tested functions for controlling āwaitā insertion (constant, linear increase, exponential/linear anneal), linear annealāgradually reducing āwaitā token frequency, proved best across multiple models and datasets.
Post-Ī± moment modulation is critical: Simply inserting āwaitā tokens leads to inertia in slow thinking. Ī±1 ensures efficient termination by replacing future āwaitā tokens with ā</think>ā, effectively forcing a shift to fast reasoning and boosting performance by up to +20% in some tasks.
8. Common Pile v0.1
The Common Pile v0.1 is an 8TB dataset of openly licensed text designed for LLM pretraining, addressing legal and ethical concerns of unlicensed data use. Two 7B parameter models trained on it, Comma v0.1-1T and 2T, achieve performance comparable to LLaMA 1 and 2, and the dataset, code, and model checkpoints are all publicly released.
9. RewardBench 2
RewardBench 2 is a new multi-skill benchmark for evaluating reward models with more challenging human prompts and stronger correlation to downstream performance. It highlights gaps in the current reward model's effectiveness and aims to support more rigorous evaluation, showing existing models score ~20 points lower than their predecessor.
10. Memorization in LLMs
This study introduces a method to quantify how much a model memorizes versus generalizes, estimating GPT models have a capacity of ~3.6 bits per parameter. By training hundreds of models, the authors show that memorization saturates with data before generalization (āgrokkingā) kicks in, and derive new scaling laws linking capacity, data size, and membership inference.
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Thanks for sharing this weekās roundup. It's both impressive and humbling to see how LLMs still struggle with complex reasoning despite all the advancements. Looking forward to your next articles!!