Document-level relation extraction (DocRE)aims to determine which relations hold between a given entity pair within a document.As a multi-label classification task, the most commonly adopted paradigm introduces a learnable threshold to distinguish positive and negative classes for an entity pair.Under this paradigm, existing losses decouple the optimization into independent positive and negative losses, which interact solely with a shared threshold.This leads to two inherent limitations: (i) threshold instability caused by conflicting gradient updates from the decoupled losses; and (ii) optimization bias exacerbated by the severe imbalance between limited positive samples and abundant negative samples inherent in DocRE, which makes the model more likely to predict that no relation exists.To address these issues, we propose the Adaptive-Threshold Global Loss (ATGL).Unlike prior work, ATGL integrates positive, negative, and threshold optimization into a unified logit space and explicitly enforces ranking constraints on their contributions to the objective.Furthermore, ATGL incorporates an imbalance-aware optimization mechanism, thereby effectively addressing the severe class imbalance in DocRE.Our ATGL serves as a general optimization objective that can be readily applied to different DocRE models.Experiments on four datasets show that ATGL outperforms other DocRE losses and achieves state-of-the-art results, while consistently improving the performance of existing DocRE models.

Cross-document Relation Extraction (CodRE) requires reasoning over scattered evidence to identify relations between target entities across multiple documents. Existing methods indiscriminately fuse target entities and the intermediate bridge entities that link them into a unified representation. This leads to intermediate evidence that often aligns with only one side of the entity pair, resulting in one-sided relation transfer contextual bias and incomplete reasoning chains. Moreover, these methods typically employ a global threshold to determine relation existence for all entity pairs, limiting the model’s reasoning performance.To address these issues, we propose DEBAR (Dual-stream Entity Bias Reduction), a framework designed to explicitly decouple and preserve bidirectional bridge evidence, combined with a novel dynamic loss optimization objective. Specifically, DEBAR employs a bridge-aware input construction strategy and a dual-stream graph reasoning network to separately encode head and tail contexts, preventing semantic interference while capturing global dependencies through iterative message passing. Furthermore, we introduce a curriculum-aware ranking optimization objective that progressively tightens classification constraints to stabilize training and enforce discriminative decision boundaries. Experiments on the CodRE benchmarks show that DEBAR achieves state-of-the-art performance while effectively mitigating cross-document contextual bias. Moreover, extensive experiments on our proposed loss across backbones confirm its generalization, suggesting it as a reliable replacement for existing CodRE losses. Code is available at https://github.com/newyuyou/DEBAR.

Multi-interest recommendation is essential for the matching stage. By generating multiple user representations, it can better cover the diverse interests derived from user interaction history. Ideally, multi-interest models should effectively identify the underlying latent factors — the specific themes, intents, or preferences — within historical behaviors. However, conventional methods typically rely on weighted aggregation (e.g., Attention), which we argue leads to over-smoothed representations. This aggregation dilutes the intensity of significant patterns that appear only locally, blending them into a blurry average. To address this, we propose DMI, a model-agnostic diffusion framework that distills precise interests by amplifying co-occurring latent factors across behaviors. Distinct from prior diffusion works that reconstruct the single next item—which risks collapsing diverse interests—DMI reconstructs the interest vectors themselves to preserve their distributional independence. To support this, we introduce a cross-transformer module that adaptively extracts interest-specific information from designated historical interactions, transforming the diffusion process from an unconditional one into a guided, interest-disentangled pathway. In addition, we design a gradient back-propagation strategy to decouple the joint optimization of the reconstruction and recommendation losses, thereby improving training stability. Extensive offline experiments demonstrate DMI's superiority over existing methods, achieving an average relative improvement of 11.2% across all metrics on Amazon Books datasets while increasing recommendation diversity by 11.8%. Successfully deployed in a real-world recommender system, DMI effectively enhances user satisfaction and system performance at scale, serving the major traffic of hundreds of millions of daily active users.

This paper studies the design of programming languages with handlers of higher-order effectful operations - effectful operations that may take in computations as arguments or return computations as output. We present and analyse a core calculus with higher-kinded impredicative polymorphism, handlers of higher-order effectful operations, and optionally general recursion. The distinctive design choice of this calculus is that handlers are carried by lawless raw monads, while the computation judgements still satisfy the monadic laws judgementally. We present the calculus with a logical framework and give denotational models of the calculus using realizability semantics. We prove closed-term canonicity and parametricity for the recursion-free fragment of the language using synthetic Tait computability and a novel form of the ⊤⊤-lifting technique.

Notions of computation can be modeled by monads. Algebraic effects offer a characterization of monads in terms of algebraic operations and equational axioms, where operations are basic programming features, such as reading or updating the state, and axioms specify observably equivalent expressions. However, many useful programming features depend on additional mechanisms such as delimited scopes or dynamically allocated resources. Such mechanisms can be supported via extensions to algebraic effects including scoped effects and parameterized algebraic theories . We present a fresh perspective on scoped effects by translation into a variation of parameterized algebraic theories. The translation enables a new approach to equational reasoning for scoped effects and gives rise to an alternative characterization of monads in terms of generators and equations involving both scoped and algebraic operations. We demonstrate the power of our approach by way of equational characterizations of several known models of scoped effects.

Abstract Notions of computation can be modelled by monads. Algebraic effects offer a characterization of monads in terms of algebraic operations and equational axioms, where operations are basic programming features, such as reading or updating the state, and axioms specify observably equivalent expressions. However, many useful programming features depend on additional mechanisms such as delimited scopes or dynamically allocated resources. Such mechanisms can be supported via extensions to algebraic effects including scoped effects and parameterized algebraic theories. We present a fresh perspective on scoped effects by translation into a variation of parameterized algebraic theories. The translation enables a new approach to equational reasoning for scoped effects and gives rise to an alternative characterization of monads in terms of generators and equations involving both scoped and algebraic operations. We demonstrate the power of our fresh perspective by way of equational characterizations of several known models of scoped effects.

This paper presents a novel formalisation of algebraic effects with equations in Cubical Agda. Unlike previous work in the literature that employed setoids to deal with equations, the library presented here uses quotient types to faithfully encode the type of terms quotiented by laws. Apart from tools for equational reasoning, the library also provides an effect-generic Hoare logic for algebraic effects, which enables reasoning about effectful programs in terms of their pre- and post-conditions. A particularly novel aspect is that equational reasoning and Hoare-style reasoning are related by an elimination principle of Hoare logic.

Inspired by algebraic effects and the principle of notions of computations as monoids, we study a categorical framework for equational theories and models of monoids equipped with operations. The framework covers not only algebraic operations but also scoped and variable-binding operations. Appealingly, in this framework both theories and models can be modularly composed. Technically, a general monoid-theory correspondence is shown, saying that the category of theories of algebraic operations is equivalent to the category of monoids. Moreover, more complex forms of operations can be coreflected into algebraic operations, in a way that preserves initial algebras. On models, we introduce modular models of a theory, which can interpret abstract syntax in the presence of other operations. We show constructions of modular models (i) from monoid transformers, (ii) from free algebras, (iii) by composition, and (iv) in symmetric monoidal categories.

Abstract Algebraic effects offer a versatile framework that covers a wide variety of effects. However, the family of operations that delimit scopes are not algebraic and are usually modelled as handlers, thus preventing them from being used freely in conjunction with algebraic operations. Although proposals for scoped operations exist, they are either ad-hoc and unprincipled, or too inconvenient for practical programming. This paper provides the best of both worlds: a theoretically-founded model of scoped effects that is convenient for implementation and reasoning. Our new model is based on an adjunction between a locally finitely presentable category and a category of functorial algebras. Using comparison functors between adjunctions, we show that our new model, an existing indexed model, and a third approach that simulates scoped operations in terms of algebraic ones have equal expressivity for handling scoped operations. We consider our new model to be the sweet spot between ease of implementation and structuredness. Additionally, our approach automatically induces fusion laws of handlers of scoped effects, which are useful for reasoning and optimisation.

Effect handlers can be composed by applying them sequentially, each handling some operations and leaving other operations uninterpreted in the syntax tree. However, the semantics of composed handlers can be subtle---it is well known that different orders of composing handlers can lead to drastically different semantics. Determining the correct order of composition is a non-trivial task. To alleviate this problem, this paper presents a systematic way of deriving sufficient conditions on handlers for their composite to correctly handle combinations, such as the sum and the tensor, of the effect theories separately handled. These conditions are solely characterised by the clauses for relevant operations of the handlers, and are derived by fusing two handlers into one using a form of fold/build fusion and continuation-passing style transformation. As case studies, the technique is applied to commutative and distributive interaction of handlers to obtain a series of results about the interaction of common handlers: (a) equations respected by each handler are preserved after handler composition; (b) handling mutable state before any handler gives rise to a semantics in which state operations are commutative with any operations from the latter handler; (c) handling the writer effect and mutable state in either order gives rise to a correct handler of the commutative combination of these two theories.