@mszll@datasci.social2026-03-29 17:45:00
Answering the dilemma of cycle lane versus shared space planning through an agent-based simulation experiment and accessibility equity analysis
https://link.springer.com/article/10.1007/s44327-026-00200-8
@arXiv_csDS_bot@mastoxiv.page2026-02-04 07:41:25
Perfect Network Resilience in Polynomial Time
Matthias Bentert, Stefan Schmid
https://arxiv.org/abs/2602.03827 https://arxiv.org/pdf/2602.03827 https://arxiv.org/html/2602.03827
arXiv:2602.03827v1 Announce Type: new
Abstract: Modern communication networks support local fast rerouting mechanisms to quickly react to link failures: nodes store a set of conditional rerouting rules which define how to forward an incoming packet in case of incident link failures. The rerouting decisions at any node $v$ must rely solely on local information available at $v$: the link from which a packet arrived at $v$, the target of the packet, and the incident link failures at $v$. Ideally, such rerouting mechanisms provide perfect resilience: any packet is routed from its source to its target as long as the two are connected in the underlying graph after the link failures. Already in their seminal paper at ACM PODC '12, Feigenbaum, Godfrey, Panda, Schapira, Shenker, and Singla showed that perfect resilience cannot always be achieved. While the design of local rerouting algorithms has received much attention since then, we still lack a detailed understanding of when perfect resilience is achievable.
This paper closes this gap and presents a complete characterization of when perfect resilience can be achieved. This characterization also allows us to design an $O(n)$-time algorithm to decide whether a given instance is perfectly resilient and an $O(nm)$-time algorithm to compute perfectly resilient rerouting rules whenever it is. Our algorithm is also attractive for the simple structure of the rerouting rules it uses, known as skipping in the literature: alternative links are chosen according to an ordered priority list (per in-port), where failed links are simply skipped. Intriguingly, our result also implies that in the context of perfect resilience, skipping rerouting rules are as powerful as more general rerouting rules. This partially answers a long-standing open question by Chiesa, Nikolaevskiy, Mitrovic, Gurtov, Madry, Schapira, and Shenker [IEEE/ACM Transactions on Networking, 2017] in the affirmative.
toXiv_bot_toot
@brian_gettler@mas.to2026-01-27 23:55:36
I'm supervising 4 undergrads working on small pieces of my research project. Their role is to comb through archival materials, to identify a modest research question, and to write a long paper answering that question. One of the students dropped by my office this morning to chat about progress. Just before leaving, they said, "I've written papers before. But they were all based on things people'd already written - I knew the answer. This is so much more fun. I feel like a de…
@burger_jaap@mastodon.social2026-02-20 06:16:18
“How many people does it take to screw in a lightbulb?
The answer – in SAPN’s case study – is an electrician, a plumber and software techs, who now all need to work together to ensure the home management system can control the proverbial lights.”
https://
@arXiv_csCL_bot@mastoxiv.page2026-03-31 10:11:57
GraphWalker: Agentic Knowledge Graph Question Answering via Synthetic Trajectory Curriculum
Shuwen Xu, Yao Xu, Jiaxiang Liu, Chenhao Yuan, Wenshuo Peng, Jun Zhao, Kang Liu
https://arxiv.org/abs/2603.28533 https://arxiv.org/pdf/2603.28533 https://arxiv.org/html/2603.28533
arXiv:2603.28533v1 Announce Type: new
Abstract: Agentic knowledge graph question answering (KGQA) requires an agent to iteratively interact with knowledge graphs (KGs), posing challenges in both training data scarcity and reasoning generalization. Specifically, existing approaches often restrict agent exploration: prompting-based methods lack autonomous navigation training, while current training pipelines usually confine reasoning to predefined trajectories. To this end, this paper proposes \textit{GraphWalker}, a novel agentic KGQA framework that addresses these challenges through \textit{Automated Trajectory Synthesis} and \textit{Stage-wise Fine-tuning}. GraphWalker adopts a two-stage SFT training paradigm: First, the agent is trained on structurally diverse trajectories synthesized from constrained random-walk paths, establishing a broad exploration prior over the KG; Second, the agent is further fine-tuned on a small set of expert trajectories to develop reflection and error recovery capabilities. Extensive experiments demonstrate that our stage-wise SFT paradigm unlocks a higher performance ceiling for a lightweight reinforcement learning (RL) stage, enabling GraphWalker to achieve state-of-the-art performance on CWQ and WebQSP. Additional results on GrailQA and our constructed GraphWalkerBench confirm that GraphWalker enhances generalization to out-of-distribution reasoning paths. The code is publicly available at https://github.com/XuShuwenn/GraphWalker
toXiv_bot_toot
@cdarwin@c.im2026-01-23 14:05:03
When an Immigration and Customs Enforcement agent shot and killed Renee Nicole Good in south Minneapolis on Jan. 7, 2026,
what happened next looked familiar, at least on the surface.
Within hours, cellphone footage spread online and eyewitness accounts contradicted official statements,
while video analysts slowed the clip down frame by frame to answer a basic question:
Did she pose the threat federal officials claimed?
What’s changed since Minneapolis became a g…
@gwire@mastodon.social2026-01-14 13:44:00
> Our conclusion is clear: AI is promising for quality assurance and marker training, but for the moment it’s nowhere near ready to take over high stakes marking.
https://ofqual.blog.gov.uk/2026/01/14/using-ai-in-mar…
