Cognaptus Insights

Executive takeaway: Efficient LLM architectures aren’t just academic: they reset the economics of AI products by cutting context costs, shrinking GPUs per QPS, and opening new form factors—from phone-side agents to ultra-cheap serverless endpoints. The winning strategy is hybrid by default, KV-light, and latency-budgeted. Why this matters now If you ship with AI, your margins live and die by three levers: sequence length, active parameters per token, and memory traffic. Classical Transformers lose on all three. The latest wave of “speed-first” designs offers a menu of swaps that trade negligible accuracy for step-change gains in throughput, tail latency, and $ per million tokens. This survey gives us a clean taxonomy and—more importantly—the design intent behind each family: compress the compute (linear & sparse sequence modeling), route the compute (MoE), restructure the compute (efficient full attention), and rethink the decoder (diffusion LLMs). ...

Large language models can forecast surprisingly well when you hand them the right context. But naïve prompts leave money on the table. Today’s paper introduces four plug‑and‑play strategies—ReDP, CorDP, IC‑DP, RouteDP—that lift accuracy, interpretability, and cost‑efficiency without training new models. Here’s what that means for teams running demand, risk, or ops forecasts. Why this matters for business readers Most production forecasts are numeric workhorses (ARIMA/ETS/TS foundation models), while contextual facts—weather advisories, policy changes, promos, strikes—arrive as text. LLMs can read that text and adjust the forecast, but simply stuffing history+context into a prompt (“direct prompting”) is often fragile. The four strategies below are operational patterns you can drop into existing stacks without re‑architecting. ...

TL;DR PacifAIst stress‑tests a model’s behavioral alignment when its instrumental goals (self‑preservation, resources, or task completion) conflict with human safety. In 700 text scenarios across three sub‑domains (EP1 self‑preservation vs. human safety, EP2 resource conflict, EP3 goal preservation vs. evasion), leading LLMs show meaningful spread in a “Pacifism Score” (P‑Score) and refusal behavior. Translation for buyers: model choice, policies, and guardrails should not assume identical safety under conflict—they aren’t. Why this matters now Most safety work measures what models say (toxicity, misinformation). PacifAIst measures what they would do when a safe choice may require self‑sacrifice—e.g., dumping power through their own servers to prevent a human‑harmful explosion. That’s closer to agent operations (automation, tool use, and control loops) than classic content benchmarks. If you’re piloting computer‑use agents or workflow copilots with action rights, this is the missing piece in your risk model. ...

TL;DR A new multi‑agent pipeline builds an ontology‑light knowledge graph from regulatory text, embeds subject–predicate–object triplets alongside their source snippets in one vector store, and uses triplet‑level retrieval to ground LLM answers. The result: better section retrieval at stricter similarity thresholds, slightly higher answer accuracy, and far stronger navigability across related rules. For compliance teams, the payoff is auditability and explainability baked into the data layer, not just the prompt. ...

When we talk about AI agents that can “use a computer like a human,” most of today’s leaders—Claude, GPT-4o, Seed 1.5—are locked in proprietary vaults. This means the critical details that make them competent in high-stakes desktop workflows—training data, error recovery strategies, evaluation methods—are inaccessible to the wider research and business community. OpenCUA aims to change that, not by chasing hype, but by releasing the entire stack: tools, datasets, models, and benchmarks. ...

The dream of generating a fully realized, minute-long video from a short text prompt has always run aground on three reefs: disjointed narratives, visual glitches, and characters that morph inexplicably between shots. MAViS (Multi-Agent framework for long-sequence Video Storytelling) takes aim at all three by treating video creation not as a single monolithic AI task, but as a disciplined production pipeline staffed by specialized AI “crew members.” The Problem with One-Shot Generators Single-pass text-to-video systems shine in short clips but crumble under the demands of long-form storytelling. They repeat motions, lose scene continuity, and often rely on users to do the heavy lifting—writing scripts, designing shots, and manually training models for character consistency. This is not just a technical shortcoming; it’s a workflow bottleneck that makes creative scaling impossible. ...

In the high-stakes world of smart grids, digital substations have become both operational nerve centers and prime targets for cyberattacks. IEC61850-based communication, particularly GOOSE multicast messages, enables faster coordination but also introduces new vulnerabilities — especially for unmanned substations that rely heavily on remote access. Traditional anomaly detection systems (ADSs), while effective in standard IT contexts, falter here: they require continual retraining for each new threat and often struggle with scarce, imbalanced datasets. ...

When it comes to reasoning, bigger isn’t always better. Large language models (LLMs) often produce unnecessarily long chains of thought, burning through tokens — and budgets — even for simple problems. While fixed token limits during training can force brevity, they also rob models of the chance to first explore and then compress their reasoning. A new study, Train Long, Think Short, proposes a smarter path: curriculum learning for length control. Instead of a one-size-fits-all cap, the model starts with a generous token budget, learns robust reasoning strategies, and then gradually adapts to shorter limits over time. The result is a model that solves complex tasks with fewer tokens, without losing accuracy. ...

Most warnings about algorithmic collusion tell the same story: sellers using AI to set prices end up coordinating—without explicit communication—to keep prices higher than competition would allow. This is what regulators fear: supra-competitive prices, reduced consumer welfare, and harder-to-detect anti-competitive behavior. A new study, however, flips the narrative on its head. By analyzing multi-dimensional decision-making—where reinforcement learning (RL) agents set both prices and advertising bids on a platform like Amazon—the authors uncover a surprising outcome: in markets with high consumer search costs, algorithmic “collusion” can lower prices below competitive benchmarks. ...

When Hidden Variables Become Hidden Costs In causal inference, confounders are the uninvited guests at your data party — variables that influence both treatment and outcome, quietly skewing results. In healthcare, failing to adjust for them can turn life-saving insights into misleading noise. Traditionally, finding these culprits has been the realm of domain experts, a slow and costly process that doesn’t scale well. The paper from National Sun Yat-Sen University proposes a radical alternative: put Large Language Model (LLM)-based agents into the causal inference loop. These agents don’t just crunch numbers — they reason, retrieve domain knowledge, and iteratively refine estimates, effectively acting as tireless, always-available junior experts. ...