AI Reasoning

Opening — Why this matters now The AI industry has grown comfortable with a flattering assumption: if a model can reason, it can plan. Multi-step logic, chain-of-thought traces, and ever-longer context windows have encouraged the belief that we are edging toward systems capable of sustained, goal-directed action. SokoBench quietly dismantles that assumption. By stripping planning down to its bare minimum, the paper reveals an uncomfortable truth: today’s large reasoning models fail not because problems are complex—but because they are long. ...

Opening — Why this matters now Multimodal AI has spent the last two years narrating its thoughts like a philosophy student with a whiteboard it refuses to use. Images go in, text comes out, and the actual visual reasoning—zooming, marking, tracing, predicting—happens offstage, if at all. Omni-R1 arrives with a blunt correction: reasoning that depends on vision should generate vision. ...

Opening — Why this matters now Declarative AI has always promised elegance: you describe the problem, the machine finds the solution. Answer Set Programming (ASP) is perhaps the purest embodiment of that ideal. But as this paper makes painfully clear, elegance does not scale for free. In an era where industrial configuration problems easily exceed 30,000 components, ASP’s biggest enemy is not logic — it’s memory. Specifically, the grounding bottleneck. This article dissects why grounding, not solving, is the true scalability killer in ASP, and why a deceptively simple idea — constraint-aware guessing (CAG) — dramatically shifts the performance frontier. ...

Opening — Why this matters now Multimodal LLMs are increasingly being asked to reason about time series: markets, traffic, power grids, pollution. Charts are rendered. Prompts are polished. The answers sound confident. And yet—too often—they’re wrong for the most boring reason imaginable: the model never actually reasons. Instead, it pattern-matches. This paper dissects that failure mode with unusual clarity. The authors argue that the bottleneck is not model scale, data access, or even modality alignment. It’s the absence of explicit reasoning priors that connect observed temporal patterns to downstream outcomes. Without those priors, multimodal LLMs hallucinate explanations after the fact, mistaking surface similarity for causality. ...

Opening — Why this matters now The industry has been quietly converging on an uncomfortable realization: raw model scaling is running out of low-hanging fruit. Training bigger models still works, but the marginal cost curve has become brutally steep. Meanwhile, real-world deployments increasingly care about inference economics—latency, throughput, and cost per correct answer—not leaderboard bravado. ...

Opening — Why this matters now For years, debates about large language models (LLMs) have circled the same tired question: Do they really understand what they’re saying? The answer—still no—has been treated as a conversation stopper. But recent “reasoning models” have made that question increasingly irrelevant. A new generation of AI systems can now reason through problems step by step, critique their own intermediate outputs, and iteratively refine solutions. They do this without grounding, common sense, or symbolic understanding—yet they still solve tasks previously reserved for humans. That contradiction is not a bug in our theory of AI. It is a flaw in our theory of reasoning. ...

Opening — Why this matters now The age of “smart” AI models has reached an uncomfortable truth: they can ace your math exam but fail your workflow. While frontier systems like GPT‑4o and Claude‑Sonnet solve increasingly complex symbolic puzzles, they stumble when asked to reason through time—to connect what happened, what’s happening, and what must happen next. In a world shifting toward autonomous agents and decision‑chain AI, this isn’t a minor bug—it’s a systemic limitation. ...

When Lateral Beats Linear: How LToT Rethinks the Tree of Thought AI researchers are learning that throwing more compute at reasoning isn’t enough. The new Lateral Tree-of-Thoughts (LToT) framework shows that the key isn’t depth—but disciplined breadth. The problem with thinking deeper As models like GPT and Mixtral gain access to massive inference budgets, the default approach—expanding Tree-of-Thought (ToT) searches—starts to break down. With thousands of tokens or nodes to explore, two predictable pathologies emerge: ...

Large language models can craft elegant arguments—but can they prove them? In law, medicine, and finance, a wrong conclusion isn’t just a hallucination; it’s a liability. The paper LOGicalThought (LogT) from USC and UT Dallas takes aim at this problem, proposing a neurosymbolic framework that lets LLMs reason with the rigor of formal logic while retaining their linguistic flexibility. From Chain-of-Thought to Chain-of-Trust Typical prompting strategies—Chain-of-Thought (CoT), Program-Aided Language Models (PAL), or self-critique loops—focus on improving reasoning coherence. Yet none of them guarantee faithfulness. A model can still reason eloquently toward a wrong or unverifiable conclusion. LogT reframes the task: it grounds the reasoning itself in a dual context—one symbolic, one logical—so that every inference step can be traced, validated, or challenged. ...

When it comes to real-world problem solving, today’s LLMs face a critical dilemma: they can solve textbook problems well, but stumble when confronted with messy, open-ended challenges—like optimizing traffic in a growing city or managing fisheries under uncertain climate shifts. Enter ModelingAgent, an ambitious new framework that turns this complexity into opportunity. What Makes Real-World Modeling So Challenging? Unlike standard math problems, real-world tasks involve ambiguity, multiple valid solutions, noisy data, and cross-domain reasoning. They often require: ...