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Cork (Ireland), will host the 12th International Conference on Degrowth for Ecological Sustainability and Social Equity in 2027 with satellite gatherings in Covilha (Portugal) and Cluj-Napoca (Romania).
Cork 2027 | degrowth.info
https://degrowth.info/en/conference/cork-2027

Cork 2027
Coming in August 2027 the 12th Conference will take place in Ireland, with satellite gatherings in Covilha (Portugal) and Cluj-Napoca (Romania). With a thematic focus on "policy, practice and culture" the event aims to help accelerate the decolonial momentum of the just transition, celebrating diverse, pluriversal voices from around the world and turning a critical eye on the extractivist policies.

Of course we humans have never had a single “shared reality,” and we never will. But there are times places when we’ve had more robust ongoing sharing and boundary-crossing and comfort with the limits of our own understanding — “shared reality” not as a fact, but as a direction.
These thoughts are still forming, and I think that’s where my thread ends for today: no answers, but yet another plea for systems thinking.
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Multimodal Policy Internalization for Conversational Agents
Zhenhailong Wang, Jiateng Liu, Amin Fazel, Ritesh Sarkhel, Xing Fan, Xiang Li, Chenlei Guo, Heng Ji, Ruhi Sarikaya
https://arxiv.org/abs/2510.09474

Multimodal Policy Internalization for Conversational Agents
Modern conversational agents like ChatGPT and Alexa+ rely on predefined policies specifying metadata, response styles, and tool-usage rules. As these LLM-based systems expand to support diverse business and user queries, such policies, often implemented as in-context prompts, are becoming increasingly complex and lengthy, making faithful adherence difficult and imposing large fixed computational costs. With the rise of multimodal agents, policies that govern visual and multimodal behaviors are …

For all of Donald Trump’s promises of an economic “golden age,”
a spate of weak indicators this week told a worrisome story as the impacts of his policies are coming into focus.
Job gains are dwindling.
Inflation is ticking upward.
Growth has slowed compared with last year.
More than six months into his term, Trump’s blitz of tariff hikes and his new tax and spending bill have remodeled America’s trading, manufacturing, energy and tax systems to his own liking.

It's Trump's economy now. The latest financial numbers offer some warning signs
President Donald Trump's economic policies are facing scrutiny as weak indicators emerge. Job gains are dwindling, inflation is rising and growth has slowed compared to last year. More than six months into his term, Trump's tariffs and tax changes have reshaped the economy. On Friday, a bleak jobs report showed a decline in manufacturing jobs, and Trump fired the head of the agency responsible for the data. Critics warn that Trump's aggressive use of tariffs and tax changes could backfire. The …

Easy Adaptation: An Efficient Task-Specific Knowledge Injection Method for Large Models in Resource-Constrained Environments
Dong Chen, Zhengqing Hu, Shixing Zhao, Yibo Guo
https://arxiv.org/abs/2512.17771 https://arxiv.org/pdf/2512.17771 https://arxiv.org/html/2512.17771
arXiv:2512.17771v1 Announce Type: new
Abstract: While the enormous parameter scale endows Large Models (LMs) with unparalleled performance, it also limits their adaptability across specific tasks. Parameter-Efficient Fine-Tuning (PEFT) has emerged as a critical approach for effectively adapting LMs to a diverse range of downstream tasks. However, existing PEFT methods face two primary challenges: (1) High resource cost. Although PEFT methods significantly reduce resource demands compared to full fine-tuning, it still requires substantial time and memory, making it impractical in resource-constrained environments. (2) Parameter dependency. PEFT methods heavily rely on updating a subset of parameters associated with LMs to incorporate task-specific knowledge. Yet, due to increasing competition in the LMs landscape, many companies have adopted closed-source policies for their leading models, offering access only via Application Programming Interface (APIs). Whereas, the expense is often cost-prohibitive and difficult to sustain, as the fine-tuning process of LMs is extremely slow. Even if small models perform far worse than LMs in general, they can achieve superior results on particular distributions while requiring only minimal resources. Motivated by this insight, we propose Easy Adaptation (EA), which designs Specific Small Models (SSMs) to complement the underfitted data distribution for LMs. Extensive experiments show that EA matches the performance of PEFT on diverse tasks without accessing LM parameters, and requires only minimal resources.
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NaviGait: Navigating Dynamically Feasible Gait Libraries using Deep Reinforcement Learning
Neil C. Janwani, Varun Madabushi, Maegan Tucker
https://arxiv.org/abs/2510.11542 https…

NaviGait: Navigating Dynamically Feasible Gait Libraries using Deep Reinforcement Learning
Reinforcement learning (RL) has emerged as a powerful method to learn robust control policies for bipedal locomotion. Yet, it can be difficult to tune desired robot behaviors due to unintuitive and complex reward design. In comparison, offline trajectory optimization methods, like Hybrid Zero Dynamics, offer more tuneable, interpretable, and mathematically grounded motion plans for high-dimensional legged systems. However, these methods often remain brittle to real-world disturbances like exter…

LegalSim: Multi-Agent Simulation of Legal Systems for Discovering Procedural Exploits
Sanket Badhe
https://arxiv.org/abs/2510.03405 https://arxiv.org/pdf/2…

LegalSim: Multi-Agent Simulation of Legal Systems for Discovering Procedural Exploits
We present LegalSim, a modular multi-agent simulation of adversarial legal proceedings that explores how AI systems can exploit procedural weaknesses in codified rules. Plaintiff and defendant agents choose from a constrained action space (for example, discovery requests, motions, meet-and-confer, sanctions) governed by a JSON rules engine, while a stochastic judge model with calibrated grant rates, cost allocations, and sanction tendencies resolves outcomes. We compare four policies: PPO, a co…

Practical considerations for assignment of photon numbers with SNSPDs
Timon Schapeler, Isabell Mischke, Fabian Schlue, Michael Stefszky, Benjamin Brecht, Christine Silberhorn, Tim J. Bartley
https://arxiv.org/abs/2510.00714

Practical considerations for assignment of photon numbers with SNSPDs
Superconducting nanowire single-photon detectors (SNSPDs) can enable photon-number resolution (PNR) based on accurate measurements of the detector's response time to few-photon optical pulses. In this work we investigate the impact of the optical pulse shape and duration on the accuracy of this method. We find that Gaussian temporal pulse shapes yield cleaner arrival-time histograms, and thus more accurate PNR, compared to bandpass-filtered pulses of equal bandwidth. For low system jitter and a…

VeriGuard: Enhancing LLM Agent Safety via Verified Code Generation
Lesly Miculicich, Mihir Parmar, Hamid Palangi, Krishnamurthy Dj Dvijotham, Mirko Montanari, Tomas Pfister, Long T. Le
https://arxiv.org/abs/2510.05156

VeriGuard: Enhancing LLM Agent Safety via Verified Code Generation
The deployment of autonomous AI agents in sensitive domains, such as healthcare, introduces critical risks to safety, security, and privacy. These agents may deviate from user objectives, violate data handling policies, or be compromised by adversarial attacks. Mitigating these dangers necessitates a mechanism to formally guarantee that an agent's actions adhere to predefined safety constraints, a challenge that existing systems do not fully address. We introduce VeriGuard, a novel framework th…

Invariant Price of Anarchy: a Metric for Welfarist Traffic Control
Ilia Shilov, Mingjia He, Heinrich H. Nax, Emilio Frazzoli, Gioele Zardini, Saverio Bolognani
https://arxiv.org/abs/2512.05843 https://arxiv.org/pdf/2512.05843 https://arxiv.org/html/2512.05843
arXiv:2512.05843v1 Announce Type: new
Abstract: The Price of Anarchy (PoA) is a standard metric for quantifying inefficiency in socio-technical systems, widely used to guide policies like traffic tolling. Conventional PoA analysis relies on exact numerical costs. However, in many settings, costs represent agents' preferences and may be defined only up to possibly arbitrary scaling and shifting, representing informational and modeling ambiguities. We observe that while such transformations preserve equilibrium and optimal outcomes, they change the PoA value. To resolve this issue, we rely on results from Social Choice Theory and define the Invariant PoA. By connecting admissible transformations to degrees of comparability of agents' costs, we derive the specific social welfare functions which ensure that efficiency evaluations do not depend on arbitrary rescalings or translations of individual costs. Case studies on a toy example and the Zurich network demonstrate that identical tolling strategies can lead to substantially different efficiency estimates depending on the assumed comparability. Our framework thus demonstrates that explicit axiomatic foundations are necessary in order to define efficiency metrics and to appropriately guide policy in large-scale infrastructure design robustly and effectively.
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