Reasoning

Opening — Why this matters now Neurosymbolic AI has long promised a synthesis: neural networks that learn, and logical systems that reason. But in practice, the two halves have been perpetually out of sync — neural systems scale but don’t explain, while symbolic systems explain but don’t scale. The recent paper DeepProofLog: Efficient Proving in Deep Stochastic Logic Programs takes a decisive step toward resolving this standoff by reframing reasoning itself as a policy optimization problem. In short, it teaches logic to think like a reinforcement learner. ...

Opening — Why this matters now By 2050, nearly seven out of ten people will live in cities. Yet most urban planning tools today still operate as statistical mirrors—learning from yesterday’s data to predict tomorrow’s congestion. Predictive models can forecast traffic or emissions, but they don’t reason about why or whether those outcomes should occur. The next leap, as argued by Sijie Yang and colleagues in Reasoning Is All You Need for Urban Planning AI, is not more prediction—but more thinking. ...

Why this matters now Large language models have grown remarkably persuasive—but not necessarily reliable. They often arrive at correct answers through logically unsound reasoning, a phenomenon both amusing in games and catastrophic in legal, biomedical, or policy contexts. The research paper VeriCoT: Neuro-Symbolic Chain-of-Thought Validation via Logical Consistency Checks proposes a decisive step toward addressing that flaw: a hybrid system where symbolic logic checks the reasoning of a neural model, not just its answers. ...

In long-horizon reasoning, large language models still behave like short-term thinkers. They can plan, but only in a straight line. Once the context window overflows, earlier intentions vanish, and the model forgets why it started. The new framework ReCAP (Recursive Context-Aware Reasoning and Planning)—from Stanford’s Computer Science Department and MIT Media Lab—offers a radical solution: give LLMs a recursive memory of their own reasoning. The Problem: Context Drift and Hierarchical Amnesia Sequential prompting—used in CoT, ReAct, and Reflexion—forces models to reason step by step along a linear chain. But in complex, multi-stage tasks (say, cooking or coding), early goals slide out of the window. Once the model’s focus shifts to later steps, earlier plans are irretrievable. Hierarchical prompting tries to fix this by spawning subtasks, but it often fragments information across layers—each sub-agent loses sight of the global goal. ...

From repetition to reasoning When early computer-use agents (CUAs) appeared, they promised to automate tedious digital workflows—clicking through files, formatting reports, or organizing spreadsheets. Yet anyone who has tried them knows the frustration: sometimes they succeed spectacularly, sometimes they click the wrong button and crash everything. Reliability, not intelligence, has been the missing link. A recent paper from Simular Research, “The Unreasonable Effectiveness of Scaling Agents for Computer Use,” shows that scaling these agents isn’t just about more compute—it’s about how we scale. Their method, Behavior Best-of-N (bBoN), turns the brute-force idea of “run many agents and hope one works” into a structured, interpretable, and near-human-level solution. ...

TL;DR Agentic performance isn’t just about doing more; it’s about going back. In GSM-Agent—a controllable, tool-using version of GSM8K—top models only reach ~65–68% accuracy, and the strongest predictor of success is a high revisit ratio: deliberately returning to a previously explored topic with a refined query. That’s actionable for enterprise AI: design agents that can (1) recognize incomplete evidence, (2) reopen earlier lines of inquiry, and (3) instrument and reward revisits. ...

TL;DR In LLM multi‑agent systems, how a model thinks matters more than how big it is. Explicit reasoning (thinking mode / CoT) creates a Persuasion Duality: sharing a model’s reasoning makes it far better at convincing others, while enabling the model’s own reasoning mode makes it far harder to convince. This shifts best practices for agent design, governance, and product UX. Why this paper matters Cognition—not just parameter count—now drives the social dynamics of agent swarms. For Cognaptus clients building agent workers (ops, compliance, research, trading), the result is practical: toggling reasoning changes not just accuracy, but influence. Your deployment choices can tilt a network toward consensus, stalemate, or resilient truth‑seeking. ...

The punchline Tree‑OPO takes something many labs already produce—MCTS rollouts from a stronger teacher—and treats them not just as answers but as a curriculum of prefixes. It then optimizes a student with GRPO-like updates, but with staged, tree-aware advantages instead of a flat group mean. The result in math reasoning (GSM8K) is a modest but consistent bump over standard GRPO while keeping memory/complexity low. Why this matters for practitioners: you can get more out of your expensive searches (or teacher traces) without training a value model or lugging around teacher logits during student training. ...

The pitch: We’ve stretched LLM “depth” by making models think longer. ParaThinker flips the axis—training models to think wider: spawn several independent lines of thought in parallel and then fuse them. The result is higher accuracy than single‑path “long thinking” at roughly the same wall‑clock time—and it scales. TL;DR for operators What it is: An end‑to‑end framework that natively generates multiple reasoning paths with special control tokens, then summarizes using cached context. Why it matters: It tackles the test‑time scaling bottleneck (aka Tunnel Vision) where early tokens lock a model into a suboptimal path. Business takeaway: You can trade a bit of GPU memory for more stable, higher‑quality answers at nearly the same latency—especially on math/logic‑heavy tasks and agentic workflows. The problem: “Think longer” hits a wall Sequential test‑time scaling (à la o1 / R1‑style longer CoT) delivers diminishing returns. After a point, more tokens don’t help; they reinforce early mistakes. ParaThinker names this failure mode Tunnel Vision—the first few tokens bias the entire trajectory. If depth traps us, width can free us. ...

TL;DR A new synthetic benchmark (INABHYD) tests inductive and abductive reasoning under Occam’s Razor. LLMs handle toy cases but falter as ontologies deepen or when multiple hypotheses are needed. Even when models “explain” observations, they often pick needlessly complex or trivial hypotheses—precisely the opposite of what scientific discovery and root-cause analysis require. The Big Idea Most reasoning work on LLMs obsesses over deduction (step-by-step proofs). But the real world demands induction (generalize rules) and abduction (best explanation). The paper introduces INABHYD, a programmable benchmark that builds fictional ontology trees (concepts, properties, subtype links) and hides some axioms. The model sees an incomplete world + observations, and must propose hypotheses that both explain all observations and do so parsimoniously (Occam’s Razor). The authors score: ...