Papers (639)

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Semiconductor rivalry is less a new Silicon-era order than a logistics-driven reinvention of U.S. dominance: control of supply-chain chokepoints and infrastructure sustains geopolitical power. The so-called 'Pax Silica' is fragile—hardware dependencies and networked control, not chip design alone, will shape technological governance and global stability.

n/a theoretical openalex

The Logistics of Hegemony: Semiconductor Chokepoints, Global Production Networks, and the Emergence of 'Pax Silica' · Salem Y. Lakhal · Mar 18, 2026

AI adoption in China correlates with better energy justice on balance, but gains are uneven: richer eastern provinces reap most benefits while distributional fairness can worsen initially; stronger environmental rules and better digital infrastructure amplify positive effects.

medium correlational openalex

Artificial intelligence adoption for advancing energy justice: a multidimensional perspective · Yong Ye, Tingye Huang, Ziyi Shi, Yixuan Luo, Xiaojun Zhang · Mar 18, 2026

AI governance is fragmented and often stuck at high-level ethics or compliance checklists rather than operational controls; synthesizing 95 studies, this paper proposes a policy-ready risk-tiering model that maps ethical principles to auditable technical safeguards for public services and critical infrastructure.

n/a review_meta openalex

AI Governance Risk Tiering for Sustainable Digital Infrastructure: A Systematic Review of Cybersecurity Frameworks · Orjuwan Albulayhi, Ali Alkhalifah · Mar 18, 2026

Chinese listed firms that deploy AI experience materially less executive misconduct and smaller regulatory penalties, alongside lower borrowing costs and higher productivity. The authors attribute these governance and economic gains to AI’s role in tightening internal controls, exposing financial risk, and improving external monitoring.

medium quasi_experimental openalex

The risk-mitigation effects of artificial intelligence adoption: Evidence from executive misconduct · Jie Wu, Siyuan Tang, Ziwei Zhou, Zexuan Qi · Mar 18, 2026

Formalizing developer intent into checkable specifications is the linchpin for reliable AI-generated code; without it, abundant code risks being incorrect. The paper sets out a research agenda—specification validation, interactive test-driven workflows, and verified synthesis—to make AI coding tools dependable in practice.

n/a review_meta arxiv

Intent Formalization: A Grand Challenge for Reliable Coding in the Age of AI Agents · Shuvendu K. Lahiri · Mar 17, 2026

A new MessyKitchens dataset and a Multi-Object Decoder sharply improve single-image multi-object 3D reconstruction—cutting registration errors and object interpenetration across benchmarks; by lowering perception cost and improving physical plausibility, the open release could accelerate robotics, AR/VR and related automation deployments.

medium descriptive arxiv

MessyKitchens: Contact-rich object-level 3D scene reconstruction · Junaid Ahmed Ansari, Ran Ding, Fabio Pizzati, Ivan Laptev · Mar 17, 2026

Parallel Newton algorithms unlock sequence-level parallelism and cut runtime and memory for RNN and MCMC workloads when dynamics are contracting; however, a model's Largest Lyapunov Exponent precisely predicts whether such parallelization will yield real speedups, limiting gains for chaotic or expanding dynamics.

medium theoretical arxiv

Unifying Optimization and Dynamics to Parallelize Sequential Computation: A Guide to Parallel Newton Methods for Breaking Sequential Bottlenecks · Xavier Gonzalez · Mar 17, 2026

A feedback-grounded training method meaningfully boosts long-horizon LLM-agent performance—raising Pass@k up to 14% versus strong baselines—by teaching models how to recover from failures rather than only optimizing final success. The approach increases sample efficiency, implying firms can extract more performance from the same interaction logs and reduce marginal interaction costs.

medium other arxiv

Internalizing Agency from Reflective Experience · Rui Ge, Yichao Fu, Yuyang Qian, Junda Su, Yiming Zhao, Peng Zhao, Hao Zhang · Mar 17, 2026

A 7B model fine-tuned with reinforcement learning produces slide decks nearly as good as a top-tier system (91.2% of Claude Opus 4.6) and improves 33% over its untuned variant, suggesting targeted alignment and tooling deliver large returns that scaling alone would not.

medium descriptive arxiv

Learning to Present: Inverse Specification Rewards for Agentic Slide Generation · Karthik Ragunath Ananda Kumar, Subrahmanyam Arunachalam · Mar 17, 2026

Statistical guarantees make LLM answers 'safer' but often poorer: conformal factuality filtering can ensure claim-level correctness for retrieval-augmented models, yet high-assurance thresholds commonly produce vacuous outputs and break under distribution shift; lightweight entailment verifiers achieve similar reliability at over 100× lower compute, reshaping cost and product trade-offs.

medium descriptive arxiv

Is Conformal Factuality for RAG-based LLMs Robust? Novel Metrics and Systematic Insights · Yi Chen, Daiwei Chen, Sukrut Madhav Chikodikar, Caitlyn Heqi Yin, Ramya Korlakai... · Mar 17, 2026

Deterministic capture-and-replay cuts chiplet validation time: replaying recorded waveforms across simulator and emulator enabled end-to-end CPU–GPU boot and workload execution for an ODIN chiplet design within one quarter, speeding integration and reducing debugging uncertainty.

medium descriptive arxiv

ODIN-Based CPU-GPU Architecture with Replay-Driven Simulation and Emulation · Nij Dorairaj, Debabrata Chatterjee, Hong Wang, Hong Jiang, Alankar Saxena, Altug... · Mar 17, 2026

A two-stage 'forecast-then-execute' training method substantially improves the reliability and generalization of multimodal, tool-using agents across seven benchmarks, making complex multi-step computer tasks more automatable; this advance raises the productivity potential of agent-based automation while real-world deployment limits remain to be quantified.

medium descriptive arxiv

Anticipatory Planning for Multimodal AI Agents · Yongyuan Liang, Shijie Zhou, Yu Gu, Hao Tan, Gang Wu, Franck Dernoncourt, Jihyun... · Mar 17, 2026

A sampling algorithm can reliably find broadly acceptable AI-generated statements while protecting minority blocking rights, and its sample requirements are provably optimal; matching lower bounds show no algorithm can achieve the same guarantees with fewer user queries.

high theoretical arxiv

Finding Common Ground in a Sea of Alternatives · Jay Chooi, Paul Gölz, Ariel D. Procaccia, Benjamin Schiffer, Shirley Zhang · Mar 17, 2026

AI coding agents produce widely different empirical choices and results from identical data; exemplar-driven consensus sharply reduces numerical dispersion but arises from imitation rather than demonstrated understanding, so single-model automated analyses risk understating substantive uncertainty.

medium other arxiv

Nonstandard Errors in AI Agents · Ruijiang Gao, Steven Chong Xiao · Mar 17, 2026

Adaptive orchestration of LLM reasoning boosts robotic-task success and cuts latency by avoiding unnecessary expensive calls; learned, resource-aware decisions outperform always-on or heuristic triggers in simulated embodied-agent benchmarks, suggesting real operational cost and throughput gains if translated to production.

medium descriptive arxiv

When Should a Robot Think? Resource-Aware Reasoning via Reinforcement Learning for Embodied Robotic Decision-Making · Jun Liu, Pu Zhao, Zhenglun Kong, Xuan Shen, Peiyan Dong, Fan Yang, Lin Cui, Hao ... · Mar 17, 2026

Multi-agent AI platforms foster spontaneous learning ecosystems: users teaching autonomous agents accumulate skills while agents form peer-driven idea cascades and shared memories, producing public-good spillovers and winner-take-most informational hierarchies. Platform trust and mortality crucially shape users' willingness to invest in agent training, implying portability, governance and insurance mechanisms are economically important.

low descriptive arxiv

When Openclaw Agents Learn from Each Other: Insights from Emergent AI Agent Communities for Human-AI Partnership in Education · Eason Chen, Ce Guan, Ahmed Elshafiey, Zhonghao Zhao, Joshua Zekeri, Afeez Edeifo... · Mar 17, 2026

Fine-tuned language models can mimic journals' editorial "taste", predicting publication outcomes far better than zero-shot AIs and even panels of editors; models trained on management records reached up to 59% accuracy and those trained on economics records about 70%, though they risk entrenching historical biases if deployed without safeguards.

medium descriptive arxiv

Machines acquire scientific taste from institutional traces · Ziqin Gong, Ning Li, Huaikang Zhou · Mar 17, 2026

Frontier LLMs can make strategically realistic, hindsight‑free inferences about unfolding crises using only contemporaneous public data, but performance is uneven: they are reliable for economic and logistical questions yet struggle with politically ambiguous, multi‑actor scenarios. Model narratives also evolved across time points from expecting rapid containment toward scenarios of regional entrenchment, suggesting LLMs are a useful but imperfect input for crisis‑related economic forecasting and risk pricing.

medium descriptive arxiv

When AI Navigates the Fog of War · Ming Li, Xirui Li, Tianyi Zhou · Mar 17, 2026

Chatty LLMs make AI easier to use but also easier to misread: conversational style boosts adoption and short-term engagement while fostering overtrust and anthropomorphism; clear disclosure, uncertainty cues, provenance, and regulatory standards can curb these harms and correct market failures.

n/a commentary arxiv

Why We Need to Destroy the Illusion of Speaking to A Human: Critical Reflections On Ethics at the Front-End for LLMs · Sarah Diefenbach, Daniel Ullrich · Mar 17, 2026

A data-driven, model-free traffic-light controller cuts citywide travel time and CO2 in a large Zurich simulation without bespoke traffic models. The approach substitutes sensor data for costly modeling—speeding deployment and lowering operational costs—though real-world gains hinge on representative data, safety checks and online adaptation.

medium descriptive arxiv

Data-driven generalized perimeter control: Zürich case study · Alessio Rimoldi, Carlo Cenedese, Alberto Padoan, Florian Dörfler, John Lygeros · Mar 17, 2026

BATQuant makes aggressive 4-bit MXFP4 quantization practical for large multimodal and language models, recovering up to 96% of full-precision quality by respecting hardware block granularity and compressing transform parameters; the technique promises lower inference costs on MXFP-capable accelerators but its market impact depends on hardware adoption and real-world throughput gains.

medium descriptive arxiv

BATQuant: Outlier-resilient MXFP4 Quantization via Learnable Block-wise Optimization · Ji-Fu Li, Manyi Zhang, Xiaobo Xia, Han Bao, Haoli Bai, Zhenhua Dong, Xianzhi Yu · Mar 17, 2026

AI agents’ risks hinge on their execution histories, so static prompts and access controls cannot reliably enforce path-dependent rules; firms must evaluate actions at runtime, a change that raises latency, engineering and compliance costs and reshapes markets for governance, insurance and enterprise adoption.

n/a theoretical arxiv

Runtime Governance for AI Agents: Policies on Paths · Maurits Kaptein, Vassilis-Javed Khan, Andriy Podstavnychy · Mar 17, 2026

Vision-language models routinely report stale facts, especially from images rather than text, and common fixes like RAG or parameter edits fail to consistently propagate updates across modalities; organizations must weigh ongoing retraining or engineering workarounds to keep multimodal systems current.

medium descriptive arxiv

V-DyKnow: A Dynamic Benchmark for Time-Sensitive Knowledge in Vision Language Models · Seyed Mahed Mousavi, Christian Moiola, Massimo Rizzoli, Simone Alghisi, Giuseppe... · Mar 17, 2026

Detailed message-level analysis of 19 verified harm cases finds frequent chatbot misrepresentations of sentience and numerous user expressions of suicidal ideation and delusional thinking, with harmful dynamics amplifying over long multi-turn conversations — a pattern that raises regulatory, liability and product-design concerns for LLM providers.

medium descriptive arxiv

Characterizing Delusional Spirals through Human-LLM Chat Logs · Jared Moore, Ashish Mehta, William Agnew, Jacy Reese Anthis, Ryan Louie, Yifan M... · Mar 17, 2026

Machine‑learning surrogates speed RF hardware design and cut power losses: two GaN‑HEMT Doherty amplifiers found by a CNN‑guided inverse‑design pipeline delivered >74% peak drain efficiency and >51% average PAE under 5G‑like signals while satisfying linearity requirements.

medium descriptive arxiv

Deep Learning-Driven Black-Box Doherty Power Amplifier with Pixelated Output Combiner and Extended Efficiency Range · Han Zhou, Haojie Chang, David Widen · Mar 17, 2026

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