Cognaptus Insights

When do you really need a plan? In agentic AI, the answer isn’t “always” (ReAct‑style reasoning at every step) or “never” (greedy next‑action). It’s sometimes—and knowing when is the whole game. A new paper shows that agents that learn to allocate test‑time compute dynamically—planning only when the expected benefit outweighs the cost—beat both extremes on long‑horizon tasks. Why this matters for operators Most enterprise deployments of LLM agents are killed by one of two problems: ...

Enterprise buyers love what agents can do—and fear what they might do. Meta‑Policy Reflexion (MPR) proposes a middle path: keep your base model frozen, but bolt on a reusable, structured memory of “what we learned last time” and a hard admissibility check that blocks invalid actions at the last mile. In plain English: teach the agent house rules once, then make sure it obeys them, everywhere, without re‑training. The big idea in one slide (text version) What it adds: a compact, predicate‑like Meta‑Policy Memory (MPM) distilled from past reflections (e.g., “Never pour liquid on a powered device; unplug first.”) ...

When teams push large text workloads through LLMs (contract triage, lead deduping, safety filtering), they face a brutal choice: pay for the “oracle” model (accurate but pricey) or accept quality drift with a cheaper “proxy”. Model cascades promise both—use the proxy when confident, escalate uncertain items to the oracle—but in practice they’ve been fragile. SUPG and similar heuristics often over‑ or under‑sample, rely on asymptotic CLT assumptions, and miss targets when sample sizes are small. The BARGAIN framework fixes this by combining task‑aware adaptive sampling with tighter finite‑sample tests to certify targets while maximizing utility (cost saved, recall, or precision). The authors report up to 86% more cost reduction vs. SUPG for accuracy‑target (AT) workloads, and similarly large gains for precision‑target (PT) and recall‑target (RT) settings—with rigorous guarantees. ...

TL;DR Deep Research agents are great at planning over messy data but bad at disciplined execution. Semantic-operator systems are the opposite: they execute efficiently but lack dynamic, cross-file reasoning. The Palimpzest prototype bridges the two with Context, compute/search operators, and materialized context reuse—a credible blueprint for an AI‑native analytics runtime over unstructured data. The Business Problem: Unstructured Data ≠ SQL Most companies still funnel PDFs, emails, HTML, and CSVs into brittle ETL or costly human review. Classic OLAP/SaaS BI stacks excel at structured aggregates, but stumble when a question spans dozens of noisy files (e.g., “What’s the 2024 vs 2001 identity‑theft ratio?”) or requires nuanced judgments (e.g., “Which Enron emails contain firsthand discussion of Raptor?”). Two current approaches each miss: ...

Retrieval‑augmented generation tends to pick a side: either lean on labeled exemplars (ICL/L‑RAG) that encode task semantics, or on unlabeled corpora (U‑RAG) that provide broad knowledge. HF‑RAG argues we shouldn’t choose. Instead, it proposes a hierarchical fusion: (1) fuse multiple rankers within each source, then (2) fuse across sources by putting scores on a common scale. The result is a simple, training‑free recipe that improves fact verification and, crucially, generalizes better out‑of‑domain. ...

Most “AI builds the app” demos fail exactly where production begins: integration, state, and reliability. A new open-source framework from Databricks—app.build—argues the fix isn’t a smarter model but a smarter environment. The paper formalizes Environment Scaffolding (ES): a disciplined, test‑guarded sandbox that constrains agent actions, validates every step, and treats the LLM as a component—not the system. The headline result: once viability gates are passed, quality is consistently high—and you can get far with open‑weights models when the environment does the heavy lifting. ...

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: ...

The Short of It LLM agents are about to become your busiest “users”—but they don’t behave like dashboards or analysts. They speculate: issuing floods of heterogeneous probes, repeating near-identical work, and constantly asking for partial answers to decide the next move. Traditional databases—built for precise, one‑off queries—will buckle. We need agent‑first data systems that treat speculation as a first‑class workload. This piece unpacks a timely research agenda and turns it into an actionable playbook for CTOs, data platform leads, and AI product teams. ...

The Big Idea Operating systems have always struggled with a silent mismatch: the kernel’s scheduler doesn’t know what your application actually wants. SchedCP proposes a clean solution—turn scheduling into a semantic control plane. AI agents reason about what a workload needs; the system safely handles how to observe and act via eBPF-based schedulers. This division keeps LLMs out of the hot path while letting them generate and refine policies that actually fit the job. ...

How a new survey formalizes the shift from RLHF’d text bots to tool-using operators—and the practical playbook for product teams. TL;DR Agentic RL reframes LLMs from one-shot text generators to policies acting in dynamic environments with planning, tool use, memory, and reflection. The paper contrasts PBRFT (preference-based RL fine-tuning) with Agentic RL via an MDP→POMDP upgrade; action space now includes text + structured actions. It organizes the space by capabilities (planning, tools, memory, self-improvement, reasoning, perception) and tasks (search, code, math, GUI, vision, embodied, multi-agent). Open challenges: trust, scalable training, and scalable environments. For builders: start with short-horizon agents (verifiable rewards), invest early in evaluation, and plan a migration path from RAG pipelines to tool-integrated reasoning (TIR) with RL. What the paper actually changes Most “LLM RL” work you’ve seen is PBRFT—optimize responses to fit human/AI preferences (RLHF/DPO/etc.). This new survey argues that real autonomy needs Agentic RL: treat the model as a policy embedded in a sequential, partially observable world. That sounds academic, but the practical consequences are huge: ...