Most benchmarks for AI tutors assume cooperative, cognitively focused students and ignore the challenging behaviors—refusal, distress, disengagement—that make real tutoring difficult. TutorCaseBench addresses this gap by mining qualitative case studies from pedagogy, educational psychology, and special education, converting them into 923 grounded tutor–student dialogues with case-specific rubrics. The results show that even top LLMs fail to align with documented interventions in nearly half of open-ended cases, revealing a critical weakness in context-sensitive support for vulnerable learners.

As populations age, robotics and ambient AI are increasingly proposed to help older adults live independently in private homes and residential aged care (RAC) facilities. This chapter frames both settings as extreme human-robot interaction (HRI) environments—cluttered, dynamic, and technically under-supported—and surveys the sensors, perception algorithms, interaction designs, privacy safeguards, and evaluation frameworks needed to make such deployments viable. Its central thesis is that success requires moving beyond lab accuracy toward real-world operational metrics, privacy-by-design, and sustained acceptability.

The paper asks who adapts to whom when a confident LLM is wrong. In a within-subjects experiment, 32 participants learned science topics from GPT-5-mini assistants whose briefings were covertly falsified at 0%, 25%, 50%, 75% or 100%. The study finds that learners absorbed roughly two-thirds of stated falsehoods regardless of overall assistant reliability, while their confidence and trust ratings failed to track accuracy. By reframing trustworthiness as a property of the human-AI pair and introducing a Human Deference Index, the work argues that sycophancy evaluation must measure human-side deference as well as model-side compliance.

Industrial organizations struggle to govern structured telemetry and unstructured documentation together because the work relies on scarce domain experts. The paper proposes contestable governance: specialist AI agents generate scored, cited arguments for metadata decisions, human stewards review and intervene before anything is committed, and an append-only record preserves full lineage. This design aims to stop unreviewed agent errors from propagating into downstream systems while making human oversight attributable and auditable.

CoWriter tackles passive human supervision in LLM writing tools by adapting cognitive-science co-authoring methodologies into a proactive agentic workflow. It deploys three specialized agents—Leader, Monitor, and Reviewer—to guide users through brainstorming, outlining, drafting, and revision while sustaining engagement and narrative control. A within-subjects study with 14 graduate-student participants shows authors feel substantially greater ownership and produce texts rated higher in human-touch qualities.

Existing computer-use benchmarks conflate planning, reasoning, and execution, making it hard to pinpoint exactly where agents fail at precise visual control. PointerBench introduces a diagnostic suite of 1,500 static screenshot–instruction pairs that isolates fine-grained GUI grounding into three tracks: spreadsheet geometry, character-level text targets, and professional-application controls. The work matters because it reveals that even top frontier models drop sharply on thin structural elements and text carets, while specialist grounding models lag further behind, exposing substantial headroom before agents can reliably manipulate dense professional interfaces.

This randomized experiment with 297 knowledge workers and 594 essays tests dynamic metacognitive scaffolds for goal specification and task decomposition in AI-assisted writing. The authors show that scaffolds improve output quality, prompting behavior, and three-day topic recall, even after the support is removed. Yet participants rated the AI as less helpful and reported higher cognitive load while using the scaffold. The paper argues this tension reveals a productive friction that designers should embrace rather than eliminate.

This paper investigates why user-stated preferences for AI responses often fail to predict their actual ratings. Using the public PRISM dataset, the authors show that the gap between stated and revealed preferences largely disappears when attributes are measured via aggregate human perceptions rather than simple text proxies, yet concrete features like length dominate ratings regardless of what users claim to value. The findings suggest that personalization systems should treat preference elicitation format as a design parameter matched to the decision at hand, not as a direct window into stable, latent utility functions.

AI coding agents can now edit native PDF financial documents while preserving issuer layouts, making identity-swapped forgeries harder to spot than image-level fakes. This paper tests whether experienced loan underwriters and processors detect such edits in a first-pass document-only review. The authors gave three hired reviewers 132 agent-edited forged PDFs derived from 47 genuine documents and checked whether their concerns actually matched the identity-altering edits. The core finding that only about one in ten judgements contained an edit-supported concern is a clear warning about relying on visual human review for financial fraud detection.

This mixed-methods study examines how university students perceive their generative AI skills changing over time and whether those perceived trajectories connect to shifts in learning practices and academic outcomes. Using a retrospective survey of 313 students and interviews with 18 across disciplines, the authors identify heterogeneous developmental patterns—most commonly rapid early improvement followed by a plateau—and find that self-reported academic gains are linked to assignment-process adaptation rather than to a specific trajectory. The work highlights uneven growth across skill domains and signals that institutions should tailor support beyond generic tool training, though the cross-sectional design cannot establish actual longitudinal change.

Player-1 attacks a core bottleneck in game agents: large-scale demonstrations cover only a narrow slice of dynamic game states, while interactive training in commercial games is hard to scale. The authors extend the computer-use action space with relative pointer motion, sustained keypresses, and composite keyboard–mouse actions. They fine-tune a VLM on just 6,044 curated gameplay trajectories (about 16 hours of control), then run multi-environment PPO in sixteen lightweight synthetic 2D/3D environments with programmatic, verifiable rewards. The central claim is that this synthetic experience yields large Minecraft gains (+27.8 percentage points for the 9B model) and generalizes to unseen games such as ViZDoom and browser-based GameWorld.

Cooperative success depends on implicit conventions, yet conventions are usually inferred only indirectly from score drops when partners are swapped. This paper introduces the convention gap $\Delta = \operatorname{mean}(P(\text{life loss})) - \text{actual life-loss rate}$ in the game Hanabi, using it to identify which situations carry implicit meaning for humans and for a hierarchy of off-belief learning agents, and to measure what removing those conventions costs. By comparing populations on a single exact literal baseline, the authors turn conventions from inferred causes of failure into directly measurable and causally testable objects.

This paper tackles the problem that LLM agents often return replies which a human requester cannot interpret because they rely on private working context unseen by the user. The authors define reply usability as the ability of a requester to understand and act on a reply from only what they have seen ($R$), and measure it with an LLM-based reader model that flags information gaps. Across eight current models, 54% of 480 replies fail, with even the best model failing 30% of the time. They show that appending explicit requirements or rewriting with a secondary model substantially lowers failures, making usability an actionable target for agent design.

Quadrupedal locomotion requires balancing command tracking, stability, and energy efficiency, yet conventional RL bakes these trade-offs into a static scalar reward that cannot change after training. PROMO proposes a preference-conditioned multi-objective framework that takes the relative priority vector $w_t \in \Delta^{K-1}$ with $K=3$ as a runtime input to a single policy, while keeping embodiment-specific locomotion priors fixed. On a Unitree Go2, the same policy transferred zero-shot modulates behavior through preference changes alone, cutting specific energy by up to $30.4\%$, position error by $38.7\%$, and peak body-attitude deviation by $59.0\%$.

While 3D Gaussian avatars render photorealistic humans efficiently, their animation pipelines are often throttled by costly per-frame neural inference. This paper introduces GALA, which shows that the animation outputs of diverse pretrained avatar networks can be closely approximated by an identity-shared linear blendshape basis extracted via distillation, coupled with a shallow coefficient predictor. By constructing the basis with block-local PCA under a rendering-aware metric and a strict memory budget, the method reduces CPU animation cost by up to three orders of magnitude and enables 60 fps on mobile devices with only modest quality loss.

This paper confronts the design problem of building dashboards for novel computational techniques whose real-world meaning is still contested. It introduces TopoLA, a system applying Zigzag Persistent Homology to learning-management data, and derives three principles for managing interpretive uncertainty: separating measurement from interpretation, graduated disclosure, and explicit acknowledgment of methodological limits. The argument is timely because fields from explainable AI to bioinformatics increasingly face the same gap between analytical power and interpretive consensus.

Structured argumentation frameworks such as ASPIC- compute whether claims are accepted under grounded semantics by chaining strict rules ($p_1,\ldots,p_n \rightarrow c$) and defeasible rules ($p_1,\ldots,p_n \Rightarrow c$), but interacting with them has traditionally required writing symbolic literals and move primitives. ABDA-NL solves this usability barrier by wrapping the ABDA engine in a natural-language web interface that renders arguments, attacks, and discussion-game explanations in plain English, and by using a large language model strictly as a translator and research assistant rather than a reasoner. Because the deterministic engine alone constructs arguments and assigns acceptance labels, users can explore and extend formal scenarios without losing logical guarantees.

The paper identifies a vocabulary gap in trustworthy AI delegation: current oversight conflates action-coordinating (cybernetic) and understanding-coordinating (epistemic) language. It argues that without checkable reasoning, human approval becomes a rubber stamp, and proposes that every consequential choice must carry a testable reversal condition. The core operational contribution is ORRCF — a deliberation-recording convention — paired with a two-part reconstruction test for auditing delegation records.

Remote caregiving robots could help older adults age in place, but conventional motion-mapping teleoperation forces caregivers into slow, unnatural postures that cause muscle fatigue. This paper proposes a lightweight Spatio-Temporal Graph Convolutional Network (S-ST-GCN) that recognizes four single-arm daily-living actions from an RGB camera and triggers preset robot trajectories, decoupling the operator's motion from the robot's execution. A finite-state machine filters misclassifications before execution. While still a proof-of-concept, the framework offers a more intuitive alternative to direct motion-tracking teleoperation for assisting with activities of daily living.

ShapDBM addresses the fragmentation problem in Decision Boundary Maps (DBMs) by transforming data into Shapley space before applying dimensionality reduction. This creates more compact decision zones that reflect model behavior rather than raw data distribution, enabling high-quality visualization of complex datasets like SVHN where traditional data-space DBMs fail.